How to find the right start to your cloud strategy with Inventory & Readiness Assessment.
Sprint-based, modular migration with AI support: for fast results, high quality and low risk.
“Data as a Product” as a basis for new business models and data-driven processes in the cloud.
What counts after the migration: multi-cloud, FinOps, upskilling — and how to ensure long-term flexibility.

Andreas specializes in developing scalable and efficient data solutions. He helps companies fully modernize their data infrastructures and brings experience both in legacy technologies and in the modern data stack such as Snowflake, Databricks, dbt and Power BI. Through his focus on process automation and data-driven decision-making processes — also in the context of generative AI — he enables companies to be more agile and future-proof.
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Laurence specifically supports companies in modernizing their data infrastructures and using new technologies effectively — in particular through her extensive experience with tools such as Databricks. With her STEM background, she explores complex technical relationships precisely and purposefully. Their approach combines deep technical expertise with a clear understanding of the specific requirements, contexts and objectives of users and organizations.
Cloud migration is not a technical endpoint, but the starting point of a strategic transformation. The mere presence in the cloud does not yet create a competitive advantage — the decisive factor is how cloud architectures and data management principles are used to achieve business goals. taod therefore makes a clear distinction between the technical move and the actual transformation. From more efficient processes to data-based decisions and the development of new business models.
A cloud migration should not be planned as a marathon, but as a series of targeted sprints. In each sprint, a functional unit is transferred to the new environment. Accompanied by automated data reconciliation, data quality reporting and AI-supported expertise. A hybrid rollout, in which old and new solutions are operated in parallel, minimizes failure risks and delivers measurable ROI in early phases.
Vendor lock-in can be effectively reduced through two measures: multi-cloud strategies and open standards. Those who rely on vendor independence from the outset retain technological flexibility and can react agilely to new market developments. Complemented by FinOps principles, in which engineering, finance and business jointly manage cloud spending, the result is a cost structure that can be planned in the long term.
AI fulfills two functions in the migration process: It accelerates the migration itself, through automatic pattern recognition, variance analysis and automation of central migration steps. And that is the goal: The cloud is the prerequisite for economically viable use of AI, as it provides computing capacity, specialized tools and pre-trained models on-demand. Without the cloud, many AI projects would simply be too expensive or barely feasible.
Each migration is preceded by a readiness assessment: a structured inventory of the existing IT landscape, governance structures and team capabilities. On the basis of a maturity model, strengths, weaknesses and the need for action become visible. Only then should measurable goals be defined and migration projects prioritized according to added business value.

Learn how to stay in control of your costs with Databricks, Snowflake, and Fabric.
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