OLAP or simply OnlineAnalytical Processing is the process that allows users to conduct
multidimensional interaction analysis operations on real-time business data.
This includes performing operations such as drilling, aggregating, pivoting,
and slicing multidimensional data.
It is necessary to create topic specific data cubes in
advance to support above operations, thereby users can inspect data in tables
or graphs and conduct real time pivoting and drilling. Having said that, let’s
consider whether OLAP is enough for catering analysis and forecasting in real
world.
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| Figure 1: Information Systems can be divided into transnational (OLTP) and analytical (OLAP). In general OLTP provide source data to data warehouse, whereas OLAP helps to analyze it. |
A mature company may have somewhat large data accumulated
about its operations. These data can be used to make certain guesses about the
business they are engage in. For example, a vehicle importer may guess what kind of people are tending to buy what
kind of vehicles. These guesses are just the basis for forecast. Then the
company could utilize accumulated data to evaluate above guesses. When a guess
evaluated to be true they can be used in forecast and when it is false they
will be re-guessed.
Above process could be referred as evaluation process, whose
purpose is to justify conclusions with evidence find in historical data. In
business analysis process, a query like the
first n customers who has purchased a vehicle from vehicle types contributed
for half of the sales volume of the company in the year the x is ubiquitous
and required some form of a computation or querying, with intermediate steps.
The requirement of building the data cube in advance, and a
limited set of actions available to perform against data cube are limiting the
analysis process in a situation like above. The data model needs to have
capabilities of reconstructing the cube or temporarily build cubes to cater
diverse analysis demands. Furthermore, most of the OLAP products are more
famous for their rich user interface; only a few has powerful online analytical capabilities.
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| Figure 2: In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema). |
So what kind of an online analytical tool could fulfill the
evaluation process? Theoretically, steps for evaluation can be considered as
computation regarding data. This computation can be defined by user and can
decide next computation actions to be taken based of the intermediate results
without having a defined model beforehand. Additionally this computation should
support performing actions on huge amount of data instead of simple numeric
computations. At this point of view, SQL is somewhat fulfilling this
requirement, but considering its own computational capability, still it has its
own limits on solving problems similar to above mentioned problem. This leaves
us in a position to think about the limitations of SQL and builds a way through
it to make a new generation of computational system for evaluation process,
namely, the real OLAP.
I’m still completely a novice in Business Intelligence. J
