Set The Level Of Detail For Dates; Specify Monthly Or Quarterly Time Arrays - IBM Cognos User Manual

Set the Level of Detail for Dates

Values for some measures in a model often apply to time periods that are not at
the lowest level in the time dimension. In such cases, you specify the actual level
of detail to which the date values apply.
For example, actual revenue values may be derived from invoice information that
accumulates on the dates that orders are filled. In contrast, sales forecasts or
budgets are usually projected for months or quarters, not days. You can specify the
level of detail to which forecasts and budgets apply in the time dimension for your
model.
The level of detail setting that you specify for a column must be supported by the
date values stored in the associated column in your data source. For example, you
cannot specify a degree of detail of day if the date values are stored only as year
and month data, such as 200602.
If a particular measure has meaning to only one level in the time dimension, you
can allocate values for that measure to lower levels.
Procedure
1. Open the property sheet for the relevant date column and click the Time tab.
2. In the Degree of detail box, select the date level appropriate to the measure in
your data source.
For example, if the source contains a measure that provides monthly forecast
values, click Month.
3. Click OK.
Example - Aligning the Date Dimension with Available Data
Source Measures
You want to map date dimension categories to the correct measures in a data
source.
Suppose you have sales figures that are stored in the following format in your data
source:
WEEK,CUSTOMER,SALES_REP,TOTAL_SALES 20060208,Fresh Air Lte 4,Francoise
LeBlanc,4977.99 20060215,Fresh Air Lte 4,Francoise LeBlanc,2955.85
The date values are specified in YMD format, but the associated measure values
are actually weekly sales summaries by sales representative.
Procedure
You specify a Degree of detail setting of Week so that you report the correct
values.

Specify Monthly or Quarterly Time Arrays

Your transactional data is stored as quarterly or monthly values, but in general,
you roll up this information into yearly results. It may be more efficient to define
the columns in your model as members of a time array, rather than as individual
measures. A time array consists of four or twelve adjacent columns that contain
quarterly or monthly values for one year.
Chapter 3. Data Sources for Your Model
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