When selecting and information management platform, there are two ways to proceed. The first involves general purpose databases that can be optimized for analytics and the second is the adoption of an analytical data warehouse that is developed purely for business intelligence and analytics use. The differences between the two are quite broad even if not always obvious from the start. While general purpose DBs customized for business intelligence applications can enable organizations to get access to the information they require, the way to optimize analytics and ensure support for more complex and diverse analytics is to adopt a framework developed for BI and analytics specifically. The reasons include the ability to expand use and add data sources more easily, take advantage of a wider variety of analytics, increase storage and take advantage of various data latency needs, scalability, etc. And even though traditional database technologies may provide some of these features, the reality is that in most cases they are fairly limited when it involves BI and analytics optimization.