Felix Mutzl
Data + AI + Strategy
Munich, Germany
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When not talking about Data & AI, Felix is a proud father and explores playgrounds and skate parks in the city. He spent the last couple of years advancing data-driven and digital transformations at international manufacturing companies: hands on, both, in code and on the shopfloor.
Throughout this time he observed that the transformation into a data + ai company requires more than “just” technology. So today, Felix helps companies across Europe in building sustainable transformation programs by translating business vision into technology and vice versa as a Data & AI Strategist at Databricks.
Felix lives close by Munich in Germany.
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Composable AI and its impact on Enterprise Architecture
Software ate the world and AI is eating software. Recent years are an epic ride of innovation that gave life to a host of new technologies and pretty much every (software) product - whether old or new - has to have some “AI magic” label. Most of these efforts, however, aren’t sustainable as “the AI” often is a bolt-on feature. The rise of AI-centered applications requires composable building blocks and impacts the data and application layers of modern enterprise architecture. Simply because this is the forefront of AI driven economic force enterprises need to get the foundations right and empower its people now! This talk examines what’s changing, what stays the same and how AI systems unlock business value drawing from hands-on examples in supply chain and human resources.
1. Status quo: AI lives in the data layer
2. Trend: composable enterprise AI
3. Big picture: integration of AI into the economy
(developer) productivity and data intelligence
"Hey, we just bought this awesome AI and it solved all of our problems" - said no one ever. Yes, we definitely live in exciting times to be in tech and I still write 90% of my code myself. Yes, assistants, co-pilots etc reduce the time I spend on stackoverflow et al, but is this already it? The field of tech is developing at a breathtaking speed - boosted by (the many promises of) AI - and we're witnesses to a user experience that's about to fundamentally change. Together, we dive into the shift from "general intelligence" to "data intelligence", lessons learned along the way, practical examples and what this means for knowledge workers across the spectrum: from data professionals to developers to business analysts and customer support.
Weaving Databricks into SAP Business Fabric
One year after the announcement of SAP Datasphere and its data ecosystem let’s have a closer look at the worlds brought together with this partnership. Many enterprises store their most critical transactional business data in SAP technologies. At the same time, Databricks’ unified data platform often is the place where large amounts come together from a plethora of sources in order to build analytics and AI applications. In this talk we’ll take a closer look at how these two worlds come together in practice. We’ll also have a look at how to unleash data for industry examples in manufacturing and retail.
Architectural angles on building a Data Lakehouse in Manufacturing
20 mins lightning talk A Lakehouse architecture brings the performance and reliability of a data warehouse to the scalability and cost-performance of a data lake. This paradigm significantly simplifies how to build out any data platform and in particular with respect to the internet of things and its large amount of data that needs to be processed.
The enterprise architect’s view: AI builds business applications and deploys them on your data & AI
20 mins lightning talk We’re in the middle of a massive transformation of entire economies through data & AI. Today, business applications usually are the “incumbents” while data platforms are the “new kids on the block” that bring data from multiple data sources i.e. applications together and build AI, which, in turn, are consumed by business applications and users. Generative AI already helps us write text and improve code. So what happens once AI builds entire applications and starts to deploy them? Well, it needs to be deployed somewhere and the data platform holding the AI might just be the best place for it. And this includes compliance with security requirements as well as a seamless integration into business processes whilst ensuring that data can flow. When this happens, the data platform gets elevated from being a data consumer to a foundational runtime for any type of IT application - all while optimizing for the use and development of AI. In this talk we’ll explore some of the consequences and possible scenarios this development can lead into.
Democratising Data + AI: from small specialised teams to every knowledge worker in the organisation
It all starts with (high quality) data: knowledge and insights are vital in realising business opportunities and the hype around Generative AI is evolving from a bag of proof of concepts (PoCs) to productive applications. In the past, both, business intelligence (BI) and AI were only accessible to a few specialised teams with the necessary skills (for ex writing SQL, building dashboards, understanding algorithms). But the future is bright for knowledge workers in organisations of all sizes as new patterns emerge and in this talk we're going to dive deeper into the architecture concept of the "Data Intelligence Platform": using metadata, semantics and GenAI to democratise the access to insights to every user with natural language as the ultimate interface. In addition to the concept, we're going to have a look at a reference implementation (live!) and discuss lessons learned over the past 12-18 months.
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