Everywhere in the world, professional communication is critical to the success of projects right from the start. Business engineering combines business expertise and technology in data and analytics projects. Regardless of the applied technology and the industry context, data and analytics projects need business engineers to act as a bridge between specialist departments and IT for communication and organisation purposes.
The bridge between professionalism and technology
OUR GOAL IS YOUR CORPORATE SUCCESS
Business engineering provides a coordination and communication link between specialist departments and IT. With our industry-specific focus, we are able to understand the core business processes of our customers and use innovative requirements engineering methods to translate models into professional IT requirements. Our business engineers ensure that dedicated project plans are in line with the overall analytics strategy.
Our certified business engineers combine skills in developing methodical solutions in requirements engineering with technological expertise in the data and analytics context. This allows us to generate the maximum added value for your company.
Requirements engineering involves the recording, documentation and management of requirements with the aim of creating technical concepts or user stories that are agreed with all relevant stakeholders.
DnA strategy consulting
We can help you successfully implement your data analytics strategy. Based on your personal starting point, we focus on the specialised information needs, technological trends and organisational challenges of the next few years.
Together with you, we develop use cases and starting points within the framework of analytics ideation based on modern design thinking methods. Our data & analytics experts evaluate the collected ideas with you in terms of their business value and technical and organisational feasibility. In this way, we create the basis to then get started with you and generate added value for your company.
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Requirement survey, project planning and target definition are typical phases in business engineering that are also the foundation of every data and analytics project. adesso offers effective methods for project initialisation with the two interaction room formats IR:bi and IR:analytics. By leveraging these methods, we can clarify the technical information requirements and define the data sources, architectures and organisational framework conditions that will establish the basis for a successful project launch.
The Interaction Room (IR) is a room that can be physically entered, where all relevant stakeholders, such as specialist departments, IT and management, come together and develop a common understanding of the current situation.
This is achieved by visualising relationships on describable maps (canvases), which are developed in the IR. This provides a transparent overview to everyone involved. Our interaction room coaches work with you using pragmatic models to develop the relevant project content: information needs, business objects, systems and data
The canvases are processed iteratively: starting with the survey of information needs (features), they are then prioritised by the group. The associated business objects, the source and target systems and the dependencies on other features are worked out for each feature. After that, the cost and value drivers are described using annotation symbols.
Throughout the workshop, the Interaction Room coaches advise and guide the group of stakeholders, bringing the knowledge of the participants to the fore and enabling them to achieve the best possible result. After the last iteration of the canvas processing, the next action steps can be derived, which establish the basis for successful implementation.
The aim of IR:bi is to achieve a common understanding between the specialist department, IT and management in terms of specialist information needs and the data sources they require.
The aim of IR:analytics is to identify feasible analytics use cases. We work together with you to find out how valuable information can be acquired from growing data volumes and which specific use cases emerge that can be applied to your business model or used to expand it in a productive manner.