Modernizing data architecture is essential to address challenges such as fragmented data management, unreliable legacy systems, and ad-hoc sharing. An effective data mesh approach decentralizes data management, empowering domain teams to handle data efficiently and collaborate seamlessly. This shift from monolithic architectures to a distributed model enhances enterprise-wide data governance.
T-Systems is transforming data management with its data mesh approach, setting itself apart through its comprehensive, tailored strategy. Unlike traditional methods that rely on centralized data warehouses and monolithic architectures, T-Systems promotes decentralized, domain-oriented data ownership, enabling teams to manage data within their specific domains. This facilitates the creation of data products and self-serve platforms, while supporting federated governance and integration.
As pioneers of data spaces for over five years, we offer comprehensive, industry-specific solutions tailored to your unique needs. We have successfully executed 50+ cognitive analytics projects, managing vast, distributed data with productive AI systems and zero outages. Our team of industry experts, including specialists in public, health, transport, and auto sectors, adheres to the highest data security standards and ethical guidelines. We leverage our AI/ML expertise and scalable deep-learning factory to swiftly deliver reusable, high-quality solutions. Our 1,200 data experts, Deutsche Telekom connectivity expertise, and vendor-agnostic approach help unlock the full potential of every project.
AI is on every client's roadmap, but there's often a gap between ambitions and data quality, with siloed, insufficient data stalling initiatives. We've addressed this through data mesh, emphasizing data ownership and long-term responsibility. At T-Systems, we help clients adopt data mesh, ensuring proper governance and maximizing data as a strategic asset in complex scenarios.