The volume of data in companies is growing rapidly – yet without a clear structure, high-performance platforms and intelligent analytical methods, its potential remains largely untapped.
We help you to manage your data reliably, process it efficiently and analyse it to your advantage. This enables you to lay the foundations for data-driven decisions, automated processes and successful AI projects.
Full transparency, instead of data silos
We are creating a unified data management platform that enables you to consolidate scattered data sources and always use reliable, up-to-date information to inform your decisions.
Making data systematically usable
From collection to analysis: we help you to capture, process and model data in a structured way, so that you can efficiently analyse even large volumes of data and identify relevant insights immediately.
Data & AI: Making data AI-ready
With a scalable data management and data science architecture, we lay the foundations for the successful and secure deployment of artificial intelligence.
These disciplines are intertwined and reinforce one another:
Together, they enable end-to-end data value creation.
With our integrated approach combining data management and data science (and AI-ready data), you can build a future-proof, scalable data architecture and embed data-driven decision-making throughout your organisation.
We support your organisation on this journey – in a structured and transparent manner, delivering clearly measurable added value.
We work with you to analyse your current data landscape and identify the use cases that offer the greatest potential for your business. This creates a clear, realistic path to better decisions and successful AI projects.
The European Space Agency faced a classic data management challenge: heterogeneous data warehouses and no standardised language for data across directorates. Working with novaCapta, the ESA has built a central data factory based on Microsoft Azure, which, as a single source of truth, enables real-time decision-making and forms the basis for future AI projects.
Our approach is based on Microsoft’s verified Team Data Science Process (TDSP) methodology, which uses the data science lifecycle to structure the project in a way that allows for flexible and iterative adaptation.
AI models are only as good as the data available to them. We turn your data into the essential foundation for successful AI projects.
From identifying specific use cases, through a proof of concept, to a successful roll-out: at every stage, we keep in mind how you can already actively utilise interim results. This is how we create added value even whilst the project is still underway.
We are Microsoft Solutions Partner for Data & AI (Azure). This specialisation validates our extensive experience in planning and delivering secure, scalable analytics solutions that enable our customers to make the most of their data resources.
Our structured processes for quality, information security and data protection are further evidenced by our ISO certificates.
As a Fabric Featured Partner, novaCapta is part of an exclusive group comprising less than one per cent of Microsoft partner companies in Germany.
Our inclusion in this exclusive Microsoft programme confirms our leading expertise in consulting, implementation and optimisation of Microsoft Fabric.
Data management, data science and artificial intelligence complement each other perfectly and are closely interlinked
Data management forms the foundation for successful AI and data science projects, as it encompasses data collection, storage and management to provide high-quality data for analysis. And vice versa: AI can significantly optimise your data management.
There is also a reciprocal relationship between artificial intelligence and data science: AI applications require well-prepared and structured data to recognise patterns and make predictions. In turn, data science uses statistical models generated by AI to analyse data and gain insights; furthermore, AI serves as a useful tool and can efficiently support data scientists in their day-to-day work.
Application scenarios and examples of the interplay between data management, data science, AI, and big data include, among others:
Optical Character Recognition (OCR): Text recognition, or automatic character recognition, converts printed or handwritten text from images into machine-readable text. This allows you, for example, to easily digitize forms.
Workforce Planning: Using real-time data—such as incoming orders or material shortages—you can automatically generate precise forecasts of required capacity to plan your workforce accurately.
Optimized Route Planning: Using data science applications, you can optimize your routes—for example, for delivering goods or for sales routes—by integrating existing and predictable orders into resource-efficient routes.
Sentiment Analysis: With sentiment analysis, you can automatically evaluate the sentiment of a message’s author and categorize it as “positive,” “negative,” or “neutral,” for example. This enables your customer service representatives to respond appropriately to customers’ sentiments.
Dynamic Pricing: In the field of e-commerce, data science helps you determine the optimal price. Based on customer purchasing behavior and competitors’ price adjustments, the optimal price for your products is calculated at all times.
Custom Named Entity Recognition: Identify and extract predefined entities from unstructured text. This allows, for example, the most important information from contracts or insurance claims to be clearly presented.
Text Classification & Summarization: Automatically sort texts into user-defined categories and extract the most important information from a text. For example, customer inquiries can be prioritized by urgency and assigned by topic.
Speech Recognition & Translation: Transcribe audio streams and files in real time into text that your applications, tools, or devices can display and utilize. The application recognizes the language and can provide a live translation if needed.
Our approach to data science projects is based on Microsoft’s verified Team Data Science Process (TDSP) methodology, which uses the data science lifecycle to structure the project in a way that is flexible and can be adapted iteratively.
Our goal is to create added value for you at every stage of the project. That’s why we view data as an equal partner in the process—not just as an end result. At every step—from collection and standardization to analysis—we keep in mind how you can directly use the data to optimize your business processes.
Microsoft Fabric brings together all enterprise-relevant data and analytics tools in one central location.
This offers you the following benefits:
Everything in one place: Microsoft Fabric combines data integration, data preparation, data analysis, and visualization into a unified platform. All data is stored centrally and is immediately available to all tools. This centralization makes it much easier for you to manage and analyze large volumes of data.
Cost Reduction & Increased Efficiency: Because the solution is SaaS-based, you pay only for the resources you use and can optimize your infrastructure costs. In addition, this means you don’t have to worry about technical operations and maintenance. Centralizing everything on a single platform also helps reduce costs and increase your employees’ efficiency.
Improved collaboration: Data pipelines, dashboards, and reports can be easily shared across teams, and workflows and data models can be versioned and reused for other projects. Thanks to integration with Microsoft Teams, your employees can collaborate directly without having to switch platforms.
Scalability & Flexibility: As a SaaS platform, Microsoft Fabric flexibly adapts to your specific data volumes and analytics requirements—from small to large projects. You can also continue to use existing open-source workflows while taking full advantage of Microsoft Fabric.
Data Security & Governance: Microsoft Fabric offers advanced security features such as built-in data monitoring and classification to verify compliance requirements, role-based access controls, and transparent logging and auditing to ensure data integrity and confidentiality.
Integration into the M365 ecosystem: Microsoft Fabric is not only closely linked to Power BI and various Azure services, but is also directly integrated into Microsoft Teams, SharePoint, and Excel. This significantly improves and simplifies collaboration and data sharing within your organization.
My team and I would be happy to advise you on the right data management and data science solutions.
Alexander Elkin
Head of Applications & Data
