After recent efforts to mine the value of data such as data warehouses or data lakes have failed, Data Mesh emerges as the latest architecture. 

 

Data Mesh is a new way of thinking about data that is based on a distributed data management architecture. The idea is to connect data owners, data producers, and data consumers in order to make data more accessible and available to business users. The goal of Data Mesh is to improve the business outcomes of data-centric solutions while also driving the adoption of modern data architectures.

 

Theoretically, Data Mesh seems to be a promising data architecture. 

 

But how practical and feasible it is in practice? 

 

In the upcoming Worldwide AI Webinar, Patrick Klinger, the CDO Hub Lead at Mercedes-Benz will round out Data Mesh at Practice and how to enable Data Sharing at scale in Enterprises.

 

Patrick Klingler is a technology enthusiast with a passion for data and artificial intelligence. His goal is to transform companies into data-driven organizations by implementing modern approaches for data architecture, state-of-the-art AI products and democratizing machine learning as a tool. 

 

Mr. Klinger started his career at Mercedes-Benz in 2015. Since then, he was responsible for several projects and products around data & AI. Currently, he is heading the Chief Data Officer Hub, which is responsible for data strategy operationalization. In his second profession, Patrick is active as an independent Keynote Speaker for data & AI and has spoken at several national and international events such as TEDx, Dublin Tech Summit, and Big Data & AI World among others.

 

Understanding that data sharing at scale still is a big challenge for many enterprises, partly because of data silos, lack of incentives, or centralized data responsibilities.

 

In his virtual keynote at the Worldwide AI Webinar 2022 on September 29, Patrick Klinger will be giving some practical insights on how a modern data architecture based on Data Mesh and Data Products can help to overcome those impediments. 

 

He will round out:

 

✅ The basic theoretic principles of Data Mesh

 

✅ A definition and quality criteria of Data Products

 

✅ Strategic fields of action to transition toward a Data Mesh architecture

 

✅ Practical examples for Data Products

 

✅ Clarification of the most common myths about Data Mesh in practice

 

Tune in to his speech on September 29 to learn more: https://wow-ai.com/event