In their 2011 study, the Aberdeen Group showed that 43% of the enterprises indicated that making timely decisions is becoming more difficult (The Agile BI Benchmark). Managers increasingly find they have less time to make decisions after certain business events occur. This is an important observation for every BI department, because it implies that it must be possible to change existing reports faster and to develop new reports more quickly.

The business need to improve the development speed and to increase agility is probably the reason why concepts such as agile BI and self-service BI have received so much attention lately. The Data Warehouse Institute recently identified five factors driving businesses toward self-service BI (2011 TDWI BI Benchmark Report: Organizational and Performance Metrics for Business Intelligence Teams) of which factors 1, 2, and 4, relate directly to productivity and agility:

1. Constantly changing business needs (65%)
2. IT's inability to satisfy new requests in a timely manner (57%)
3. The need to be a more analytics-driven organization (54%)
4. Slow and untimely access to information (47%)
5. Business user dissatisfaction with IT-delivered BI capabilities (34%)

Many vendors of data virtualization servers have made statements claiming that deploying their products do lead to an increase of productivity and an improvement of agility in BI projects. But how? I will give two dominant reasons.

First, with data virtualization, reports are decoupled from the data sources. The effect is that it becomes easier to change the data storage solution without having to change the reports (or vice versa). For example, a generic SQL database server can be replaced by a faster analytical database server without having to change one line of code in the reports. Another example where decoupling is useful is when a reporting query is redirected away from a database that contains derived data to one that contains original data. This means that the derived data can be removed thus simplifying the entire system. Such a change is transparent to the reports. Simplification is (almost) always good for productivity and agility.

Second, if users need access to data they haven't used yet, making that data available to them using data virtualization is usually very easy, even if it concerns a NoSQL product or unstructured data. Maybe that first implementation is slow, but it works, and users can deploy it and may even experience a ROI. In parallel with the use of this first implementation, the IT department can look for a more efficient implementation (if required). Again, making changes afterwards, has no impact on the reports.

More reasons exist that explain why data virtualization makes BI systems more agile and productive, but I think these two are the main ones. For more on how DV increases productivity and agility, I refer to the webinar series entitled Agile Data Integration for BI Lecture Series.