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For example, they compare actual sales against targets and against sales in prior
periods. They examine the breakdown of sales by product, by store, by sales terri-
tory, by promotion, and so on.
Decision makers are no longer satisfied with one-dimensional queries such as
“How many units of Product A did we sell in the store in Milltown, New Jersey?”
Consider the following query, which is more useful: “How much revenue did the
new Product X generate during the last three months, broken down by individual
months, in the South Central territory, by individual stores, broken down by pro-
motions, compared to estimates, and compared to the previous version of the
product?” The analysis does not stop with this single multidimensional query. The
user continues to ask for further comparisons with similar products, comparisons
among territories, and views of the results by rotating the presentation between
columns and rows.
For effective analysis, your users must have easy methods for performing
complex analysis along several business dimensions. They need an environment that
presents a multidimensional view of data providing the foundation for analytical
processing through easy and flexible access to information. Decision makers must
be able to analyze data along any number of dimensions, at any level of aggrega-
tion, with the capability of viewing results in a variety of ways. They must have
the ability to drill down and roll up along the hierarchies of every dimension.
Without a solid system for true multidimensional analysis, your data warehouse is
incomplete.
In any analytical system, time is a critical dimension. Hardly any query is exe-
cuted without having time as one of the dimensions along which analysis is per-
formed. Furthermore, time is a unique dimension because of its sequential nature.
November of a year always comes after October of that year. Users monitor per-
formance over time, as for example, performance this month compared to last
month, or performance this month compared with performance in the same month
last year.
Another point about the uniqueness of the time dimension is the way in
which the hierarchies of the dimension work. A user may look for sales in March
and may also look for sales for the first four months of the year. In the second query
for sales for the first four months, the implied hierarchy at the next higher level is
an aggregation taking into account the sequential nature of time. No user looks for
sales of the first four stores or the last three stores. There is no implied sequence in
the store dimension. True analytical systems must recognize the sequential nature
of time.
Need for Fast Access and Powerful Calculations
Whether a user's request
is for monthly sales of all products along all geographical regions or for year-to-
date sales in a region for a single product, the query and analysis system must
have consistent response times. Users must not be penalized for the complexity
of their analysis. Both the size of the effort to formulate a query or the amount
of time to receive the result sets must be consistent irrespective of the query
types.
Let us take an example to understand how the speed of the analysis process
matters to users. Imagine a business analyst looking for reasons why profitability
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