Data warehousing is successfully creating a layer between classical operational databases and reporting applications. In the financial industry, data warehousing architectures are crucial for creating market value and supporting business activities. Business data modeling, the initial phase of a DWH project, establishes a common language among stakeholders such as departments of a bank. However, we find that methodologies followed in the financial service industry today are insufficient in practice. In addition, these methodologies do not offer solutions for typical issues that occur during development of a data warehouse inside a bank. This leads us to the following research question: Can a structured methology for the early development phase of financial DWH projects help prevent failure of these projects? The answer to this question is presented as a business data modeling methodology that establishes a basis for consistent data and clear product scope definitions. Furthermore, it enhances top management prioritization and decreases interpretation ambiguities caused by insufficient specifications and thereby helps to close the Business-IT gap.