Check this SD Times article out: Future of data analysis lies in tools for humans, not automatic systems.
"Andreas Weigend... said that “data is only worth as much as the decisions made based on that data."
This is the entire point of IT: IT Systems Support Decision-Making. The job is not to "automate" decision-making with a bunch of business rules. The job is to create systems to support decision-making by people. Buying a tool that allows "end-users" to drag and drop icons to create workflows and business rules misses the point. Automating everything isn't helpful.
Information should be classified and categorized to facilitate decision-making.
People need to be in the loop.
Management by exception can only happen when people see the data, can analyze, categorize, summarize and -- by manipulating the data -- discover outliers and unusual special cases.
Too many systems attempt to leave people out of the loop.
Business Rules
A canonical example of business rule processing is credit checks or discounts in an order processing system. This requires integrating a lot of information, and making a decision based on ordering history, credit-worthiness, etc., etc.
In some cases, the decision may be routine. But even then, it is subject to some review to be sure that management goals are being met. Offering credit or discounts is a business strategy decision -- it has a real dollar-valued impact. A person owns this policy and needs to be sure that it makes business sense.
These decisions are not just "if-statements" in BPML or Java or something. They are larger than this.
One good design is to queue up the requests, sorted into groups by their relative complexity, so a person can view the queues and either make (or confirm) the automated decisions. It's a boring job, but they're doing management by exception. They own the problems, the corner cases, the potential fraud cases, the suspicious cases. They should have an incentive payment for every real problem they solve.
You give up "real-time" because there's a person in the loop. For small value, high-volume consumer purchases, you may not want a person in the loop. Most of us, however, are not Amazon.com. Most of us have businesses that are higher value and smaller volume. People will look at the orders anyway.
All IT Systems must facilitate and simplify manual review. Even if they can automate, the record of the decisions made should be trivial to review. Screen shots or log scraping or special-purpose audit/extract programs mean the application doesn't correctly put people into the process.
"Automated" Data Mining
In most of the data warehousing projects I've worked on, folks have been interested in the idea of "automated data mining" discovering something novel in their data. For example, one of the banks I worked with was hoping for some kind of magical analysis of risk in their loan portfolio.
Data Mining is highly constrained by the implicit causal models that people already have. There's a philosophical issue with attempting to correlate random numbers in a database and then trying to reason out some theory or model for those correlations. The science of going from observation to theory requires actual thinking. There's a long analysis here: http://theoryandscience.icaap.org/content/vol9.2/Chong.html.
Indeed, the only possible point of data mining is not to discover something completely unexpected, but to confirm the details of something suspected but hidden by noise or complexity. People formulate models, they confirm (or reject) them with tools like a data warehouse with some data mining analytics.