Data Transparency is a basic need of every data worker. It is crucial to find the right data in a limited amount of time to keep the time-to-market of data and analytical products short. However, documenting and classifying data manually can be cumbersome due to the vast amount of data. In this session, we present the approach MediaMarktSaturn has taken to use LLMs and other AI models in combination with a data catalog to establish a high level of data transparency in a semi-automated way.

Target Audience: Chief Data Officers, Data Governance Managers, Data Strategists, Data Engineers, Data Scientists, Subject Matter Experts
Prerequisites: Basic knowledge of Data Catalogs and LLMs
Level: Basic

Extended Abstract:
Data Transparency is a basic need of every data worker. It is crucial to find the right data in a limited amount of time to keep the time-to-market of data and analytical products short. But with the ever-growing amount of data, it gets more and more difficult to keep up a suitable level of data transparency. In this session, we present the approach MediaMarktSaturn has taken to use LLMs and Open Source Data Catalogs to establish a high level of data transparency in a semi-automated way. With this approach, it is possible to keep the manual work at a suitable level and manage data transparency in an efficient way. Moreover, we will outline how this approach is integrated into the overall Data Governance and Data Architecture.

Supporting analytics and data science in an enterprise involves more than installing open source or using cloud services. Too often the focus is on technology when it should be on data. The goal is to build multi-purpose infrastructure that can support both past uses and new requirements. This session discusses architecture principles, design assumptions, and the data architecture and data governance needed to build good infrastructure.

Target Audience: BI and analytics leaders and managers; data architects; architects, designers, and implementers; anyone with data management responsibilities who is challenged by recent changes in the data landscape
Prerequisites: A basic understanding of how data is used in organizations, applications and platforms in the data ecosystem, data management concepts
Level: Advanced

Extended Abstract:
The focus in our market has been on acquiring technology, and that ignores the more important part: the landscape within which this technology exists and the data architecture that lies at its core. If one expects longevity from a platform then it should be a designed rather than accidental architecture. 

Architecture is more than just software. It starts with uses, and includes the data, technology, methods of building and maintaining, governance, and organization of people. What are design principles that lead to good design and data architecture? What assumptions limit older approaches? How can one modernize an existing data environment? How will this affect data management? This session will help you answer these questions.

Outline - Topics covered:    

• A brief history of data infrastructure and past design assumptions
• Categories of data and data use in organizations
• Differences between BI, analysis, and data science workloads
• Data architecture
• Data management and governance processes
• Tradeoffs and antipatterns

Learn how Lufthansa Group is leveraging AI in the sky - and how you could apply the same concepts to your business. Lufthansa Group and Google Cloud developed a cloud native application that pulls data from operational systems to the cloud for analysis by artificial intelligence and operations research for optimizing flight operations. It balances crew availability, passenger demand, aircraft maintenance status etc. Recommendations are given to Operations Control for final checks and implementation.

Target Audience: People with interest in how to apply data driven optimization of their businesses. Data Engineers, Project Leads, and Decision Makers
Prerequisites: Interest in optimizing operational processes using data. A basic understanding of managing data coming from a diverse set of operational applications
Level: Advanced

Extended Abstract:
Imagine you are an airline OpsController responsible for running the operations of an airline and you just learned that an aircraft that was supposed to leave for Frankfurt in 30 minutes has to stay on the ground longer due to a minor repair. What do you do now? Passengers have booked connecting flights from Frankfurt, otherwise hotel accommodations might become necessary. Wait for the repair and possibly delay connecting flights as well? Is such a delay okay for the crews or will some exceed their permitted working hours? Is a replacement aircraft available? If so, is there a crew for it? And what about...  

Such a small issue can trigger a cascade of thighs to consider and this can quickly exceed human capabilities in their complexity. There are more potential matches of chess possible than protons in the universe. And airline operations is an even harder problem. Highly experienced people can solve situations to a certain extent based on their wealth of experience. But wouldn't it be better if optimal decisions across multiple business processes (which still can affect punctuality and passengers) were proposed to the person in the OpsControl Center with the support of an AI?

Lufthansa Group has addressed this problem and, together with Google Cloud, created a data platform for its operational data from core business processes. This data foundation is used as input for AI models to calculate future alternative scenarios from the airline's perspective. These are calculated using optimizers that Google contributes to the project. At runtime, in addition to the operational data, a set of airline-specific business rules together with a cost function, which is also the responsibility of the airline, are given to the so-called 'Solver'.

When a request is made, several scenarios are calculated in parallel. These differ in their respective configurations and their results are summarized and the best scenarios are displayed to the person in OpsControl as a proposal for evaluation via a specially created user interface. The person in OpsControl can then transfer the necessary changes back to the operational systems for implementation in the physical world. It is very important to note that today and for the foreseeable future a human being has the final say regarding the use of AI-generated proposals.

The application presented here optimizes across different business units simultaneously and avoids the common occurrence of local optimizations that contradict each other and may have to be resolved manually. The solution is in use at Swiss Intl. Airlines, a Lufthansa Group airline. After 18 months, the use of this technology resulted in CO2 emission savings of 7,400 tons per year through reduced fuel consumption, which is equivalent to approximately 18 Boeing 777 flights between Zurich and New York City. These savings will increase significantly when the application is rolled out to the other airlines in the Lufthansa Group.

In this presentation, Christian Most and Andreas Ribbrock will explain the use case. They will also show how the Lufthansa Group uses the cloud as a real-time data integration platform to prepare data from operational systems, each of which does exactly what it is supposed to do but is not designed to process data from other systems. The challenges posed by this approach will also be described. As well as the opportunities offered by 'serverless' technologies, since the project team does not have to worry about infrastructure.

The ELM approach is a new way to get the business requirements analysis for your data warehouse and data architecture and a perfect fit to model your Data Vault.

This presentation will mimic the 6 steps needed to guide Samen- und Pflanzenhandel Willibald from their business case towards an Ensemble Logical Model – the ELM approach. Each of the 6 steps can be seen as a small part of the combined journey of business and IT as described in the ELM approach. When the Willibald case came available, we applied our ELM approach and, apart from mimicking workshops, used all our templates as a guide towards a Data Vault model based upon the Willibald business and not upon the available source systems.   

The ELM approach consists of a series of 2-4 workshops with the business people and using templates to discover the Core Business Concepts – that is what is important for the organization – and their relationships. All of the 6 steps will also communicate and document what is important for the organization from a non-technical perspective and help to understand the Ensemble Logical model fit to implement the Data Vault. 

If you ever asked yourself How can I get the business user to actively involved? How do I get requirements about which data should be integrated? And how should it be done from a business perspective?

Well, you will find answers here.

The successful establishment of low-code and self-service platforms in enterprises is not a guaranteed. Seamless collaboration among departments, management, IT, and security is crucial. The Müller Service Group has effectively tackled this challenge through the implementation of a governance framework. In this session, we will share best practices and insights from our journey towards successful governance. We will use the Microsoft Power Platform as a case study within the presentation as an example of a low-code platform at Müller.

Target Audience: Decision Makers, project leaders, IT-managers, IT-architects, data analysts, Chief Data Officers (CDOs), Chief Information Officers (CIOs), responsible individuals in Data & Analytics
Prerequisites: Basic Knowledge
Level: Basic

Extended Abstract:
In this session, we will first introduce the foundational framework crucial for the successful operation of a low-code platform, particularly within the context of the Microsoft Power Platform. Subsequently, we will delve into a detailed examination of opportunities and risks associated with operating such a platform, emphasizing the strategic significance of governance.

An additional emphasis during the presentation highlights the central role of IT in operating low-code and self-service platforms. Managers and domain experts will gain valuable insights with practical tips on successfully convincing and engaging their IT in the benefits of the low-code concept. This session provides a comprehensive overview of the potentials and challenges of low-code and self-service platforms. 

Join us as we explore the transformative potential of this governance approach for Low-Code & Self-Service-Platforms.

This talk introduces the basics of Generative AI (GenAI) capabilities in Snowflake's Data Cloud, showing how it can improve how we interact with data. We will cover three practical examples through live demos: First, using Snowflake Copilot, we'll see how to chat with data in natural language. Next, we'll look at how integrating Hugging Face models with Snowflake's Model Registry can expand its capabilities. Lastly, we will demonstrate building advanced chatbots that can search and use information from documents. This session will provide useful insights into using GenAI to revolutionize data tasks with Snowflake.

FINMA Circular 2023/01, which comes into force on January 1, 2024, requires banks to define, identify and monitor the data elements that are considered critical for the successful and sustainable provision of the institution's services or for regulatory purposes. In this presentation the CDO of Banca Stato will outline how the bank as utilized Collibra to build an effective data governance framework and achieved following benefits: 

• Improved data quality
• Increased data security
• Enhanced regulatory compliance
• Improved business agility

The landscape of Data Warehousing (DWH) is evolving, influenced by pivotal trends in GenAI and Data Products/Data Mesh. At Databricks, we believe that AI is a catalyst for simplification, enabling every user in the organization to easily interact with data. As many organizations move to decentralize their data domain, we see a high potential for data products to build the integration layer of different autonomous domains. Join this session to learn how our Data Intelligence Platform combines AI with the benefits of a lakehouse, and how we leverage data products to deliver seamless data integration across technology domains.

Prerequisites: General understanding of data architecture and data mesh is an advantage

This presentation gives an overview on the concept of Boehringer Ingelheims Data Science Academy. The Academy aims at providing data literacy to all data users and leaders. It also provides upskilling opportunities for experts in the domain. Besides the general setup, I will give an overview on the founding of the Academy. Furthermore important learnings and our further development are shown.

Target Audience: Anyone who wants to increase data literacy in their company and learn and discuss about the setup of a Data Science Academy
Prerequisites: None
Level: Basic

As more and more organizations experiment with Generative AI and deploy it in production, a crucial question emerges: Can these applications be both safe and scalable in an enterprise context? 
Cost Control, Auditing, Data protection, PII Detection, Retrieval Augmented Generation… the obstacles to deploying LLM in production are many. 
In this session, you’ll find out how LLM Mesh is helping the Enterprise to overcome these obstacles.

Prerequisites: None

Lasst den Konferenztag bei einem erfrischenden Getränk in lockerer Atmosphäre ausklingen. Trefft Branchenkolleg:innen, knüpft neue Kontakte und genießt einen entspannten Abend voller Networking & anregender Gespräche. Der perfekte Mix aus Fachwissen, Austausch und geselligem Beisammensein – wir freuen uns auf euch!

How can Data Cataloguing, Modelling, DQ and other streams join forces to create business value? The speaker shares experience from a data vendor and a manufacturing business.

Target Audience: Data Professionals and decision makers with stakes in the value chain big picture
Prerequisites: Familiarity with Data Governance concepts (Catalogue, Quality, Integration etc.)
Level: Advanced

Extended Abstract:
Data Governance can contribute local optimizations to a company's value chain, such as better data discovery via a Data Catalogue, or quality-monitored and cleansed data sets. From a 30,000 ft Data Strategy view, it is even more desirable to connect the dots for business objects frequently reused among business processes and make them available as governed, quality-controlled, easily accessible Data Products.

The speaker successfully launched a Data Governance program in a company traditionally ranking metal higher than data and will share experiences on the ongoing Data Product journey:

• Identifying scope
• Cataloging technical metadata
• Modeling a logical layer
• Managing sensitive data in a hybrid architecture
• Simplifying cross-system access

Explore the future of MLOps as we delve into building Azure ML pipelines using OOP. Discover how a generic and reusable MLOps pipeline streamlines new use case initiation. We utilize MLflow for managing the ML lifecycle and model deployments. We leverage OOP and dependency injection to build an MLOps framework, eliminating all the boilerplate and making it easy for our customers to start new use cases. Developers can reuse, inject, or utilize AutoML for training modules. This solution is an accelerator and saves significant development time.

Target Audience: Data Scientists, Engineers, DevOps and all Data Enthusiasts
Prerequisites: Basic knowledge of Data Science, Data Engineering and DevOps
Level: Advanced

Extended Abstract:
In our solution, we integrate Azure AutoML, enabling individuals without expertise in data science to effortlessly deploy state-of-the-art machine learning models onto their datasets. This facilitates the extraction of optimal models, which can subsequently be refined and customized to align with their specific requirements.

Within this session colleagues from Siemens Financial Services and b.telligent present how they are building jointly an intelligence data integration framework based on Azure Services leveraging the unique advantages of concepts such as generic loading, automated schema evolution to magnify flexibility and how they fine-tune workloads, to ensure a smooth performance and to cost-efficient workload management at the same time.

Target Audience: Technical Experts in Data Warehousing, Data & Cloud Engineering.
Prerequisites: Advanced Knowledge in ETL/ELT Processes, Azure Cloud, Data Integration a/o Data Warehousing
Level: Advanced

In this talk, we delve into the emerging field of applying large language models in the industry with focus on different techniques of augmenting large language models while preserving confidentiality of data that is used for additional context.

In this talk we will explore the fundamentals of RAG, its implications for privacy-preserving data handling, and practical considerations for its implementation on a real usecase.

Target Audience: machine learning engineers, data scientists, data engineers, privacy officers, software engineers, product owners
Prerequisites: Basic knowledge about large language models
Level: Advanced

Extended Abstract:
The rapid advancement of language models has brought in unprecedented capabilities in text generation, comprehension, and interaction. However, as these models become more integrated into industry applications, the tension between leveraging vast amounts of data for improved performance and ensuring the privacy of that data has intensified. This presentation delves into the practical application of Retrieval Augmented Generation (RAG) as a novel solution to this challenge, focusing on its implementation within large language models to balance data utility with confidentiality.

Retrieval Augmented Generation (RAG) combines the generative powers of large language models with the precision of information retrieval systems. By fetching relevant information on demand and incorporating it into the generation process, RAG models can produce more accurate and contextually relevant outputs. This technique not only enhances the model's utility by broadening its knowledge base but also introduces a mechanism for controlling the exposure of sensitive data.

The core of our discussion will center on the technical underpinnings of RAG, detailing how it retrieves information and the algorithms that govern its integration with generative models. We'll examine the architecture required to facilitate this interaction, the processes for indexing and retrieving content, and the methodologies for ensuring that the retrieved data does not compromise privacy.

Furthermore, we'll present a case study demonstrating RAG's application on a real project. This example will illustrate how RAG can be tailored to specific privacy requirements and regulatory standards, showcasing its versatility and effectiveness. We will also discuss the challenges faced during these implementations.

In this session Marcel will talk about how organizations are realizing a data shopping process with the correct technology, organization and business processes. The session will unveil the role of each technology component from data catalogs and data marketplaces to data virtualization and data pipelining. Furthermore, the role of data governance and organizational change management will be explored and customer reference architectures will be provided to illustrate an exemplary end to end data shopping process.

Target Audience: CTO, CIO, Head of BI, Data Engineer, Data Scientist, Data Steward
Prerequisites: Basic understanding of Data Mesh (optional)
Schwierigkeitsgrad: Advanced

Zum Abschluss der TDWI München 2024 laden wir euch zu einer interaktiven Wrap-Up-Session ein, um die wichtigsten Erkenntnisse und Highlights der vergangenen drei Tage gemeinsam Revue passieren zu lassen. In dieser abschließenden Runde werden Klaus-Dieter Schulze und Philipp Loringhoven ihre persönlichen Highlights und Key Takeaways aus den zahlreichen Vorträgen, Workshops und Diskussionen teilen.

Wir freuen uns darauf, gemeinsam mit euch die TDWI München 2024 abzuschließen und wertvolle Einblicke für zukünftige Veranstaltungen zu gewinnen. Eure Meinung ist uns wichtig – gestaltet mit uns die Zukunft der TDWI München!