Data warehouse projects can quickly get out of hand in terms of size and scope because it is difficult for IT managers to deny requests from users and executives to change or expand the design. This can make it difficult or even impossible to deliver BI systems on time and under budget. Large data warehouses need to integrate data from across the organization—different hardware, operating systems, databases and applications—and integration efforts can be time-consuming and costly. A successful BI application takes good advance planning, organization-wide sponsorship and input, and doing your homework to choose the right tools and the right vendors to partner with.

Recent surveys indicate that one of the greatest challenges companies face in building and maintaining a data warehouse is moving the data into the warehouse. Operational data is typically stored in multiple tables and consists of codes and abbreviations, making it difficult to access for decision support. A simple invoice, for example, may contain data from over a dozen different files. More often than not, operational systems also contain inconsistent data. An inventory system may store data as “Male” and “Female,” while a system used by sales stores the same information as “M” and “F.” Given the circumstances, most would agree that unleashing end users on a data warehouse without first cleansing or transforming the raw transactional data that populates the warehouse would be a poor idea. Data quality can also affect data warehouse performance. With data warehouses and data marts, the analogy is: ‘garbage in, garbage out.’ You won’t find the trends and relationships you’re looking for in the data unless you feed your query and analysis tools with the right information. A little knowledge, or the wrong knowledge, can be a dangerous thing. It can give you an incomplete or flawed picture of your customer or your business.