Data Warehousing technology covers a wide range of products, tools and techniques.
Probably more important than any of them though is to have a good understanding of why you're considering it, and what information is highest priority. As anand4all implies, unless your organisation has a lot of money, and more importantly time to be thorough, a traditional large-scale data warehouse containing many instances of data extracted from your OLTP systems is overkill, certainly as an initial goal.
A better approach, especially if your organisation hasn't used much in the way of Management Information tools previously, is probably to start with one or two priority areas (either business critical information, or areas where reporting load is interfering with OLTP operations), study these information requirements carefully, and extract the appropriate data from your OLTP systems out into targeted data marts. The tool selection for the reporting/analysis of this data should involve the end-users as much as possible, but bear in mind that once users actually start using it for real, this will affect their understanding of their own requirements considerably; try not to lock any future strategy into a particular tool-set's capabilities.
If you do start off with the data-mart route, try to ensure that where dimensions are shared between one business area and another (and hence one mart and another), e.g. time, or product hierarchies, that their implementation is compatible; this will mean that if there is a future need to merge this information (either in a larger data warehouse, or simply in a report needing information from >1 mart), it is as simple as possible. In jargon terms this could be described as having conforming dimensions across federated data marts

I hope the above rambling is of some use to you Jui.
Good luck, and happy warehousing
