One of those tools will have to be Online Analytical Processing (OLAP). The purpose of this article is to provide a generalized framework in which the data skimmed online can be processed and eventually embedded within the day to day infrastructure of a business.

Of course, the level of detail, investment and sophistication will vary between a small firm and the multinational organization. However the basic concepts still remain the same.

The key difference between OLAP and other data collation mechanism is that it undertakes an approach that is simultaneous navigational, hierarchical and relational. This is a far more comprehensive method than merely examining particular strands and informational patterns. If used correctly, it can enable large-scale interpretation of vast amounts of discrete and varied pieces of business intelligence into a technically relevant set of causes and effects.

In other wards the decision maker is enabled to gather seemingly unrelated pieces of information and thereby create a story that will ultimately translate into profits for the business. Often the challenge for a business decision making process is not that there is not enough information or that it is not even accurate. No, the real challenge does know what the information means for you and your people within the organization. Online Analytical Processing is just one way of making things way simpler.

It goes without saying that there will inevitably raise certain intrinsic problems where the business is using complex systems to resolve even more complex questions. One of them is accuracy: by its very nature the use of often seemingly remote data involves compromise by way of aggregates and assumptions. The thinking behind these assumptions and the criteria for selective reporting in making predictive analysis has to undergo very stringent scrutiny otherwise one single misjudgment could end up producing increasingly inaccurate data. Decisions based on bad information are almost always bad decisions.

The second problem is concerned with the manageability of the database system once it has evolved. The effective use of business intelligence requires that managers ought to be able to access the analytical process tool data on a fairly regular basis. This is not a big problem if the firm is fairly straightforward. However for the more complex business organizations, there will be practical concerns about access and maintenance.

In conclusion it would be arguable that online analytical processing vastly improves the effectiveness of business intelligence by extending the variety of sources of information and collating them in such a way as to make sense to the frontline and the key decision makers. It also addresses some of the more bureaucratic bottlenecks commonly associated with the processing of complex data. It would therefore be very shortsighted to fail to include OLAP within the mix of systems and tools that are used to collect and analysis in business intelligence modules.