Status Quo Analysis
Analysis of your use cases, data sources and target architecture.
Data integration for BI, AI, and GenAI
We integrate your data holistically—for operational applications, classic BI, modern analytics, and GenAI. Our focus: sustainable architecture, production-ready operation, and rapid added value for specialist departments.
Our Approach: Data Integration Implementation with Data Vault 2.0, Kimball and DataOps
We begin each project with a detailed analysis of your use cases, data sources and target architecture. Close collaboration with your business teams is key to ensuring the integration is perfectly aligned with real-world requirements. Wherever possible, we leverage your existing technologies and add only what is necessary to achieve optimal data integration.
Architecture & Modelling
Implementation of Data Vault 2.0 and Kimball models, development of semantic layers as well as vector provisioning and API integration for both operational and analytical scenarios.
DataOps & Automation
Introduction of DataOps principles to ensure continuous quality and stability. CI/CD, automated testing, monitoring and alerting guarantee production-grade performance of your data pipelines.
Governance & Security by Design
Integration of governance, lineage and security-as-code directly into the development process, including roles, policies and documentation. This ensures that data remains traceable, secure and compliant.
Your First Step Architecture Assessment
A performant and scalable data architecture is the backbone of every data-driven organisation. Yet many lack a clear overview: Which systems and interfaces truly matter? Where are the bottlenecks or technical debts? In our Architecture Assessment, we analyse your existing data landscape – from infrastructure and integration to governance and security – and evaluate how well it is prepared for future requirements. The result is a clear set of recommendations on how to modernise, simplify and align your architecture with new data and AI use cases.
From Data Integration to GenAI: Making Your Data Architecture Future-Proof
From architecture and orchestration to governance and GenAI, we help you establish the right concepts, technologies and processes for a scalable, secure and sustainable data integration.
Designing a Future-Proof Data Architecture
Create a scalable target architecture that seamlessly unites structured and unstructured data from source to use case. We support you in selecting the right storage, integration and analytics components to break down data silos and build a unified data foundation.
Building Efficient Data Processes
Find the right components for your data workflows, from ETL and ELT to orchestration and monitoring. We help you ensure performance, automation and transparency across your data pipelines for greater efficiency and reduced complexity.
Prepare your data for GenAI & governance
Get your data ready for controlled use in GenAI applications. We establish governance, lineage, and automation mechanisms that ensure data quality, compliance, and traceability—for responsible use of AI.
What is modern data integration?
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FAQs about Data Integration
In addition to the IT and data teams, specialist departments should also be involved at an early stage. Especially those where data is generated or used: sales, finance, operations, and, if necessary, legal/compliance. In the case of GenAI or application integration, it is also worth consulting product owners or development teams.
We start with a brief analysis of your use cases, target systems, and existing sources. We then develop a streamlined concept and implement it immediately, focusing on automation, governance, and operation. Many customers start with a specific use case and then scale up.
Usually analytical integration targets BI, AI, and GenAI with structured, harmonized data. Application integration is event- or API-based and supplies operational systems in real time. Both can run on the same tools—we advise you on which is appropriate in each case.