Being able to distinguish between an application-driven intelligence solution and a data-driven intelligence application is important because the development methodology is different. Although both can use agile development methods, there are a number of key differences. For example, Ken Collier in his book Agile Analytics discusses the difference between agile development for data warehousing and business intelligence and that of traditional applications. All in all, you have to know what is different (skill sets, methodology, resources, time frame) for building or using an application-driven intelligence solution vs. a data-driven intelligence application.
Operationally, SLA decisions and IT infrastructure decisions for data-driven intelligence applications probably differ from application-driven intelligence applications. For example, current customer orders must be protected at all cost, so significant data protection, including strong disaster recovery, needs to be in place. However, where external Web data is ingested internally for a big data analysis, it may not make any sense to provide a high level of data protection as the data could simply be re-ingested from its original sources.
You don't have to worry about trying to fit a project into a particular definition. Big data is a hot topic, but what is it exactly? Doug Laney (now of Gartner) introduced the popular concept of volume, variety and velocity. This is a very powerful and useful idea, but it does not precisely define what is big and what is little. Moreover, size alone does not determine value. Using a data-driven intelligence approach causes you to think about its overall value and the software technology that needs to be applied. If the project seems to fit the bill of big data, then call it that. If it does not but still delivers value, go ahead. Be benefit-driven, not label-driven.
Recognize that collectively data-driven intelligence is the engine that makes more data-centric IT possible. This collective perspective encompasses all the pieces and gives a better sense of the total value that results when viewing the world through a data-driven intelligence lens.
This is a short introduction to a broad subject and will require further discussion both from a general perspective as well as using specific product illustrations. Now, application-driven intelligence tends to focus on operational business processes. Data-driven intelligence tends to fulfill the needs of management information systems.
The big difference is that the proper use of data-driven intelligence will help organizations switch from intuition-based to data-based decisions--a transformation that should have a positive effect at all levels of an organization.