Data warehousing (DW) and business intelligence (BI) projects are a high priority for many organizations as they seek to empower more and better data-driven decisions and actions throughout their enterprise. Such firms want to expand their user base for BI, data management and analytics (DM/A), and data discovery so fewer users are making uninformed decisions. Similarly, users are demanding high-quality BI reports.
However, DW projects are highly complex and inherently risky. Among their many tasks, the project manager who leads the data warehouse team must identify all data quality risks associated with a particular data warehouse implementation.
Download this new Ten Mistakes to Avoid to learn how organizations can sidestep QA problems that many other DW projects have experienced. Tips offered will help ensure satisfaction as DW and DM/A teams plan and implement new functions and capabilities. These time-tested recommendations may save significant money, time, and staff resources and improve results from the DW application being developed.