Huge amounts of data accumulate in nearly all areas. More and more companies want to do more than just collect this data and prepare it for reporting; they want to extract added value from it through advanced analytics. This includes the acquisition of helpful knowledge about market conditions, the promotion of cross/up-selling, or forecasting and decision-making based on this data. Our aim is to generate a competitive advantage for you from your collected data.
In modern data & analytics projects, the requirements for communication with the respective departments are precisely determined, the data platform is integrated and provided, and the data volumes are analysed and evaluated. In this way, we guarantee you detailed insights into the data and enable future forecasts.
The use of AI methods, such as machine learning or deep learning, is an important foundation for these kinds of forecasts. Get the most out of our services in this area, which are designed to meet specific needs as the integration of big data platforms and data science solutions continues to increase. Our wide range of data and analytics services covers a combination of business engineering and the provision of data platforms – structured as a data warehouse or semi-structured as a data lake – and the related area of ‘data science’.
We are familiar with the most common technologies on the market and provide our customers with end-to-end support in data & analytics. We develop your DnA strategy together with our business engineers, who are specialised in specific industries. Our consultants have the required expertise of different business processes ranging from for example financial or logistics.
In terms of data management and big data engineering, we construct large, modern data platforms, such as data warehouse, data lake or hybrid scenarios, and operationalise them with or for our customers. If the data is available, we use specific methods from the field of data science and advanced analytics to perform an AI-based evaluation. Our experts identify suitable use cases, develop them in the DataLab and operationalise them on the appropriate data platform.