Manage Data Elements
Data Elements provide a sharable definition of a data item or field. These descriptors can be defined at either the global or project level. A global data element can be shared by multiple projects through the import process.
Within a project, you will create a data element for each different data item that is to be acted upon during the disguise process. A data element by itself is nothing more than a name that is used to reference the data item during disguise definition.
Data element names must be unique within a project.
From the Data Elements view, you can add a new data element, edit, rename, or delete an existing data element, disable one or more data elements, import a global data element, or import data elements from a file. You can also view the data element summary. Global data elements must be imported into projects before they can be used by the disguise process.
Source data identifiers locate the actual data associated with a data element. The identification process matches the source data identifiers defined in the data element to the field and column names defined within the metadata. To define a data element, specify the processing options and source data identifiers required for that data element. At execution time, processing options determine how data values are handled during normalization and disguise. Data element options include selecting a processing type, value alignment, invalid/long data options, null values, and references. All processing options have default values.
Source data identifiers provide field names and patterns that are used to locate instances of data at disguise time.
You can save your data element before you specify any source data identifiers, but it is not usable since it cannot locate data until source data identifiers are defined. Rules can also be created using a data element without defined source data identifiers. However, again, it cannot locate data.
The normalized format is the format upon which the disguise rule is built. Normalization removes any differences in data that are attributable to the format in which the data is stored or the platform where the data originated. Before the disguise rule is applied, the data from the source field is converted to a standard format so the disguise rule will always see the data in the same format. Since the disguise rule only sees the data in the normalized format, it returns consistent results. After the rule is processed, the disguised value is converted back from the normalized format to the format of the source data.
Unicode is the format for normalized data, so all disguise techniques will act on Unicode data.