The need to deliver accurate information quickly is the fundamental challenge for Business Intelligence. The production of high quality information in a useful format takes time—data must be acquired, cleansed, modeled, and stored over continuous update and enhancement cycles. If any of these aspects of properly managing data are given less than appropriate attention, the quality of the information suffers—you take a short cut for speed of delivery and risk a reduction in the quality of the final product.

Even the highest quality information is of little value if it comes too late to be helpful. So the pressure is on meeting the business requests for information now, not in the several days or weeks it might take to define requirements, update the data model, develop ETL processes, test, validate, and finally make the information available in the data warehouse. Businesses often cannot wait and so they develop alternatives for acquiring and "managing" their own data. These alternatives, though they may answer the need for speed, inevitably result in both redundant data and inconsistent information.

Over time, technology and business groups have developed strategies and techniques aimed at coming closer to aligning managed data and the much faster business cycles. Improvements in traditional data storage engines, including the development of multidimensional models and ETL tools have helped. Iterative and agile development methodologies have given BI more of a continuous improvement than waterfall behavior and have made the environment more nimble. Still, there remained a gap where IT could not respond quickly enough to business demands, and businesses did not have the skill and discipline to sufficiently manage high quality data.