I don't know of any books or papers specifically on this but here are some practical ideas: capture time, capture measurement and goals data, improve quality and consolidate disparate sources. If a data warehouse is supposed to be a historical, integrated, read-only database of subject data, then make sure you can detect and keep history, that you have adequate quality data so it's worth reading, that you reduce the number of data sources and that you rationalize reference data in preparation for the warehouse. One way to proactively reduce the number of sources is when building operational systems to ensure that project teams reuse existing databases where possible instead of always creating "their own" new database and loading "their own" data into it.
The warehouse is based on time so time must be available for event or fact data (which it always is) and for reference or dimension data (which it always isn't). Of course, define your calendars; you may have several, such as a fiscal, periodic and Gregorian calendar.
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