The relevance of decision support and the related potential has increased in the past years fostered by the rising number of data sources available inside and outside companies and total data points. The available data sources, especially company-external differ in their explanatory power and the effort needed to extract and process the data. To structure the available data and enhance the decision support process, we develop a construction model based on the principles of design science research for the development of a data landscape, which enables the definition of goal-oriented research questions and the identification of related available data in and outside of the company. The framework is empirically tested in the field online advertising. The application reveals the landscapes contribution to the decision making which leads to economic valuable results.