As you just learned, although the terms data and information refer to similar things, they aren't really interchangeable as there is some difference in their meaning and spirit. Talking about data, as a rule, involves its structure, format, storage, as well as ways in which you can access and manipulate it. In contrast, when talking about information, you mean food for your decision-making process. So, data can be viewed as low-level information structures, where the internal representation matters. Therefore, the ways in which you can extract useful information from data entirely depend on the structure and storage of that data.
The following diagram gives a conceptual view of delivering information from different data sets:
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As you can see from the figure, information can be derived from different data sources, and by different means. Once it's derived, though, it doesn't matter where it has come from, letting its consumers concentrate on the business aspects rather than on the specifics of the internal structure. For example, you might derive some pieces of data from the Web, using the Oracle Database's XQuery feature, and then process it as native database data.
To produce meaningful information from your data, you will most likely need to perform several processing steps, load new data, and summarize the data. This is why the Business Intelligence layer usually sits on top of many data sources, consolidating information from various business systems and heterogeneous platforms.
The following figure gives a graphical depiction of a Business Intelligence system. In particular, it shows you that the Business Intelligence layer consumes information derived from various sources and heterogeneous platforms.
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It is intuitively clear that the ability to solve problems is greatly enhanced if you can effectively handle all the information you're getting. On the other hand, extracting information from data coming in from different sources may become a nightmare if you try to do it on your own, with only the help of miscellaneous tools. Business Intelligence comes to the rescue here, ensuring that the extraction, transformation, and consolidation of data from disparate sources becomes totally transparent to you.
For example, when using a Business Intelligence application for reporting, you may never figure out exactly what happens behind the scenes when you instruct the system to prepare another report. The information you need for such a report may be collected from many different sources, hiding the complexities associated with handling heterogeneous data. But, without Business Intelligence, that would be a whole different story, of course. Imagine for a moment that you have to issue several queries against different systems, using different tools, and you then have to consolidate the results somehow—all just to answer a single business question such as: what are the top three customers for the preceding quarter?
As you have no doubt realized, the software at the Business Intelligence layer is used to provide a business-centric view of data, eliminating as much of the technology-specific logic as possible. What this means in practice is that information consumers working at the Business Intelligence layer may not even know that, say, customer records are stored in a Lightweight Directory Access Protocol (LDAP) database, but purchase orders are kept in a relational database.
