Take the example of a fraud application for a bank. An event-driven approach today will monitor activity and if it spots a set of activities that matches a set pattern of fraudulent activity, will kick off an exception process to go and manually discover whether in fact the activity was in some way illegal. The trouble is that fraudsters will quickly change their patterns of operating to stay ahead of this kind of detection. And as fraudulent activity increases, so does the number of instances that require manually sorting at the end of the trading period. This quickly becomes prohibitive.
With a much deeper level of functionality, an application can go one step further. But that requires:
* Business intelligence that takes data and maps different relationships to spot particular scenarios
* Bn integration infrastructure where multiple sources of data can be pulled together, including traditional data warehouses
* A complex event processing architecture to look for sophisticated patterns of activities
* And business process management to kick off both machine and human oriented exception processes
* Rich analytics capabilities to empower end users to ask and answer any question.
Business intelligence plays a vital role in generating discoveries in information, new insights, opportunities and risks in various data sources, then subsequently determining which of those models appeared to be of value and, downstream of the application, pooling all of the data together from multiple sources to see what has actually happened.
Analytic applications are also able to determine different scenarios that are worth modelling and by integrating with a lot of different data sources come up with a set of hypotheses that look for relationships between data and the different patterns that occur that might indicate fraudulent activity. That analysis can be fed into a series of rules that are running on a product line which are monitored as trading activity happens. Then when certain conditions are met, the system can trigger a number of exception processes. One of these processes might initiate a series of analysis sessions that look at particu3lar situations to determine whether they were actually fraudulent and further inform the model.
Intelligent business
The trouble is that traditional business intelligence applications are rarely up to the job. They tend to rely on data warehouses or data cubes, and routines run nightly to crunch data so that systems can easily access it in a fast, supposedly on-line manner in preformatted reports.
That’s fine in a production reporting environment, where they allow users to drill into a particular path in the data, following logical drill down paths. Using a sales example, the user might want to look at national sales, then regional, territory, rep sales, and finally drill into an individual’s performance. But at any point in that analysis if the user wanted to know what was affecting sales and look at other variables that weren’t prepared for them, they would need to go back to the IT department and ask them to add some data for analysis or reconfigure a new data cube for them. In which case the user is held up for hours, if not days or weeks.
Traditional BI systems are architected this way because they assume that users are interested in a certain set of predefined variables and are looking at known issues and questions. But take this into a more complex environment, and users don’t necessarily know the questions they want to ask, nor the data sources they want to interrogate. They want to be able to pull different data sources into their analytic application on the fly and easily visualise the data that the system is presenting to them. And, needless to say, they want to do it fast. In addition, the predictive business wants to put these tools into the hands of business users in their day-to-day work without having recourse to the IT department whenever they want to add a new data source.
We’re entering a new era of pervasive business intelligence which will see BI everywhere as it relates to users – with experiences as simple and intuitive as using Google Earth or iTunes – and BI everywhere as it is built into processes, systems and applications. That’s a powerful combination.
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