Defining a Data Source in Disclosure Management

The Suffix transformation in Disclosure Management is used to append a definable text to a value and write it to a new column.

Instead of a fixed value, the Suffix parameter can take its value from a document variable (see Using Document Variables in Text Parameters).

Example

New column name: Item name_Group

Source column: Item name

Suffix: _Group

Column before transformation:

New column after transformation:

The transformation type Join in Disclosure Management is used to connect the values of two selectable columns and write them to a new column.

Example

New column name: Account_with_Transaction_type

First source column: Account number

Second source column: Transaction type

Separator: #

Columns before transformation:

New column after transformation:

The transformation type Replace in Disclosure Management is used to replace text in a source column and write the result to a new column. All occurrences of the text entered in Remove text (mandatory) are replaced by the text entered in Insert text (optional). If you leave Insert text empty, the matched text is removed without replacement. You can use this, for example, to generate clean programming values from a raw source column.

Instead of a fixed value, the Remove text and Insert text parameters can take their values from a document variable (see Using Document Variables in Text Parameters).

Example

New column name: SN-Nummer

Source column: Column1

Remove text: Programmierung_

Insert text: SN-

Source data before transformation:

New column after transformation:

The transformation type Multiply in Disclosure Management is used to multiply each numeric value of a source column by a defined Factor and write the result to a new column. For example, you can use the factor 1000 to convert values reported in thousands into single units, or the factor -1 to flip the sign of a column. Non-numeric values are skipped (left empty in the result column) and empty cells remain empty. The result is formatted according to the same decimal and grouping rules as the Sum aggregation.

Example

New column name: Wert x -1

Source column: Column5

Factor: -1

Source data before transformation:

New column after transformation:

The transformation type Substring in Disclosure Management is used to extract a defined portion of a text value from a source column and write it to a new column. The extraction begins at the position entered in Start (1-based, so the first character is position 1) and returns the number of characters entered in Amount of characters. If the requested number of characters exceeds the remaining length of the value, the entire remaining text is returned. If the start position is beyond the length of the value, the result is empty. Both fields accept whole numbers greater than 0.

Example

New column name: Laufende Nummer

Source column: Column1

Start: 16

Amount of characters: 5

Source data before transformation:

New column after transformation:

The transformation type Split in Disclosure Management is used to split a text value in a source column into parts using a defined separator and write the part at a specified index into a new column. Use this transformation to extract a specific segment from a composite key or delimited value – for example, to read the country code out of a value such as "Berlin;DE;10115".

After selecting Split as the transformation type in Disclosure Management, the following additional fields are displayed:

Example

New column name: Country code

Source column: Location

Separator: ;

Section: 2

Source data before transformation:

New column after transformation:

The transformation type Take over in Disclosure Management processes a source column row by row, top to bottom, and writes a value into a new column for every row:

Use this transformation to flatten grouped or header-style source data – for example, when a parent account name appears only once at the top of a group and the rows below are left blank.

Example

New column name: Account group

Source column: Group header

Source data before transformation:

New column after transformation:

You can use the Sum aggregation in Disclosure Management to total all numerical values in the value column – grouped according to the source column to which the total function is applied.

Example

Source column: Column2

Columns before aggregation:

Columns after aggregation:

You can use the Filter aggregation type in Disclosure Management to import only values that match the defined filter condition.

Instead of a fixed value, the Filter parameter can take its value from a document variable (see Using Document Variables in Text Parameters).

The condition must be entered manually into the Filter field. The filter value or text must be placed in double inverted commas.

The following arithmetical and logical operators are available for defining the filter condition:

Arithmetical operators for text filters:

Arithmetical operators for number filters:

Logical operators:

Filter conditions can be linked with and and or.

Example

Source column: Transaction type

Filter expression: contains "110"

Column before aggregation:

Column after aggregation:

The Filter not empty aggregation type in Disclosure Management removes every row whose value in the selected source column is empty. A value counts as empty when it is null, an empty string, or contains only whitespace (spaces, tabs). Rows with a non-whitespace value in the source column are kept unchanged. Other columns are not modified.

Example

Aggregation name: Remove empty keys

Source column: Programming key

Rows before aggregation:

Rows after aggregation:

The Filter numeric aggregation type in Disclosure Management keeps only rows whose value in the selected source column is numeric. A value counts as numeric when it can be parsed as a decimal number according to the connector's number format settings (consistent with the Sum aggregation). Negative values and decimal values count as numeric. Empty cells, text values, and values containing non-numeric characters are not counted as numeric and the corresponding rows are removed. Other columns are not modified.

Example

Aggregation name: Keep numeric amounts only

Source column: Amount

Rows before aggregation:

Rows after aggregation: