Business Intelligence (BI) software products, open source or not, mainly focus on the developers and system integrators building BI applications, and not much on the end users. The end user’s problem is pretty straightforward: “I am sitting on a mountain of data accumulated into various data silos in my organization, and I need help making sense out of it; I need a tool that helps me derive actionable insights.” End users don’t want to write code or SQL and MDX queries. They don’t want to mess with complex regression models or machine-learning data mining models. They want a tool that has some notion of the business questions they ask, and helps them analyze their data in the context of those questions—hence the term business intelligence.

The “I” in BI should be about intelligence, not infrastructure. Most of current BI technology focuses on the “infrastructure” components—database, OLAP, workflow, data mining, and reporting. But just by having these components in one place, you don’t become intelligent. You still need data models optimized for your specific types of analyses, you still need to find the appropriate data mining and statistical models for your problem space, you still need the right visualizations to interactively analyze and publish your data in a way that mere mortals can understand it. This is the real “I” in BI.

Open source BI software highlights this difference interestingly because open source by its very nature commoditizes the infrastructure of BI, pushing the proverbial “value up the stack” to components that actually provide intelligence. Lately, there has been a lot of activity in the open source BI space—JasperReports, Pentaho, and BIRT are few of the popular projects. However, what is critical is to understand how all this activity relates to the common end users of BI.

End users want to use BI software to analyze different data assets spread across their organization, and easily share the results of their analysis to facilitate better decision-making. To connect this need with all the activity in the open source BI, one still needs an application development team to put together all the necessary components into a coherent BI application. Now, this is a much better scenario than a few years ago, where you needed to purchase an expensive and proprietary enterprise software product just to get this process started. In that sense, open source is reducing the cost of application development, hence making BI more accessible to the masses. However, given how fragmented the open source BI components are, cost of putting together all these components can still be significant. Add to that the need of understanding business requirements and the applicability of various models and algorithms to specific business questions, and this can still be a daunting task.

If we look at the typical BI software infrastructure, it usually boils down to four key technologies:
1) Database and ETL technology to load and build data marts.