Date Profiler

1.3.6.4 Date Profiler

The Date Profiler analyzes a Date attribute, and shows the distribution of date values in that attribute in terms of:

Day of the week
Day of the month
Day of the year
Month
Year

A Valid/Null view is also included. Invalid dates are by definition Null, as any data value in a DATE attribute must be a valid date.

Use the Date Profiler to see if there are any unusual trends in your Date attributes - for example to see if there is a default date such as 01/01/1970 that has commonly been used instead of a real date value.

The following table describes the configuration options:

Configuration	Description
Inputs	Specify a single Date attribute.
Options	None.
Outputs	Describes any data attribute or flag attribute outputs.
Data Attributes	The data attributes are: `[Attribute Name].dayofweek`: adds the day of week. Possible values: Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday `[Attribute Name].dayofmonth`: adds the day of the month in a new attribute. Possible values are 1-31. `[Attribute Name].dayofyear`: adds the day of the year in a new attribute. Possible values are 1 Jan - 31st Dec. `[Attribute Name].month`: adds the month in a new attribute. Possible values are January-December. `[Attribute Name].year`: adds the year in a new attribute. Possible values are any four-digit year. Note that splitting out the date values in the way above may be useful for downstream processing, for example, if you want to write out the data and perform matching based on the day, month and year values in separate attributes.
Flags	None.

The Date Profiler looks for trends in batches of records with date values. It therefore requires a batch of records to produce its statistics. It must run to completion before its results are available, and is not suitable for a process that requires a real time response.

When executed against a batch of transactions from a real time data source, it will finish its processing when the commit point (transaction or time limit) configured on the Read Processor is reached.

The following table describes the statistics produced for the Day in Week view:

Statistic	Description
Day in week	The day of the week (Sunday-Saturday).
Count	The number of records with dates that fell on that day of the week.
%	The percentage of records with dates that fell on that day of the week.

The following table describes the statistics produced for the Day in Month view:

Statistic	Description
Day in month	The day of the month (1-31).
Count	The number of records with dates that fell on that day of the month.
%	The percentage of records with dates that fell on that day of the month.

The following table describes the statistics produced for the Day in Year view:

Statistic	Description
Day in year	The day of the year (for example, 1st Jan).
Count	The number of records with dates that fell on that day of the year.
%	The percentage of records with dates that fell on that day of the year.

The following table describes the statistics produced for the Month view:

Statistic	Description
Month	The month (January - December).
Count	The number of records with dates that fell in that month.
%	The percentage of records with dates that fell in that month.

The following table describes the statistics produced for the Year view:

Statistic	Description
Year	The year.
Count	The number of records with dates that fell in that year.
%	The percentage of records with dates that fell in that year.

The following table describes the statistics produced for the Valid/Null view:

Statistic	Description
Valid	The number of records with a valid date in the DATE attribute analyzed.
Null	The number of records with a null value in the DATE attribute analyzed.

Clicking on the Additional Information button from the Valid/Null view shows the statistics as percentages of the total number of records analyzed.

Examples

In this example, the Date Profiler analyzes the distribution of dates in an attribute storing the date of the last payment made by a Customer. In this case, the user is most interested in the distribution of dates across years. The year summary:

Year	Count	%
2003	369	18.4
2002	303	15.1
2001	250	12.5
2000	219	10.9
1999	174	8.7
1998	159	7.4
2004	152	7.6
1997	126	6.3
1996	103	5.1
1994	73	3.6
1995	42	2.1
1993	27	1.3