Add/Modify Condition

Use the Add/Modify Condition dialog box to define the criteria used to determine if a record is an "exception". Conditions consist of one or more logical statements that evaluate the value of a field.

  • Predefined Conditions—Select a predefined condition or retain "<custom condition>" in the dropdown to create a new condition.
  • Name—A name for the condition. The name can be anything you like. Since the condition name is displayed in the Business Steward Portal, you should use a descriptive name. For example, "MatchScore<80" or "FailedDPV". If you try to give a new condition a name that is identical to an existing condition but with other characters appended to the end (for example, "FailedDPV" and "FailedDPV2"), you will be asked whether you want to overwrite the existing condition as soon as you type the last character that matches its name (using our example, "V"). Say "Yes" to the prompt, finish naming the condition, and when you press OK or Save, both conditions will be visible on the Exception Monitor Options dialog box. The new condition will not overwrite the existing condition unless the name is 100% identical.
  • Assign to—Select a user to whom the exception records meeting this condition should be assigned. If you do not make a selection in this field, the excepion records will automatically be assigned to the user who ran the job.
  • Data domain—(Optional) Specifies the kind of data being evaluated by the condition. This is used solely for reporting purposes to show which types of exceptions occur in your data. For example, if the condition evaluates the success or failure of address validation, the data domain could be "Address"; if the condition evaluates the success or failure of a geocoding operation, the data domain could be "Spatial", and so forth. You can specify your own data domain or select one of the predefined domains:
    • Account—The condition checks a business or organization name associated with a sales account.
    • Address—The condition checks address data, such as a complete mailing address or a postal code.
    • Asset—The condition checks data about the property of a company, such as physical property, real estate, human resources, or other assets.
    • Date—The condition checks date data.
    • Email—The condition checks email data.
    • Financial—The condition checks data related to currency, securities, and so forth.
    • Name—The condition checks personal name data, such as a first name or last name.
    • Phone—The condition checks phone number data.
    • Product—The condition checks data about materials, parts, merchandise, and so forth.
    • Spatial—The condition checks point, polygon, or line data which represents a defined geographic feature, such as flood plains, coastal lines, houses, sales territories, and so forth.
    • SSN—The condition checks U.S. Social Security Number data.
    • Uncategorized—Choose this option if you do not want to categorize this condition.
  • Data quality metric —(Optional) Specifies the metric that this condition measures. This is used solely for reporting purposes to show which types of exceptions occur in your data. For example, if the condition is designed to evaluate the record's completeness (meaning, for example, that all addresses contain postal codes) then you could specify "Completeness" as the data quality metric. You can specify your own metric or select one of the predefined metrics:
    • Accuracy—The condition measures whether the data could be verified against a trusted source. For example, if an address could not be verified using data from the postal authority, it could be considered to be an exception because it is not accurate.
    • Completeness—The condition measures whether data is missing essential attributes. For example, an address that is missing the postal code, or an account that is missing a contact name.
    • Consistency—The condition measures whether the data is consistent between multiple systems. For example if your customer data system uses gender codes of M and F, but the data you are processing has gender codes of 0 and 1, the data could be considered to have consistency problems.
    • Interpretability—The condition measures whether data is correctly parsed into a data structure that can be interpreted by another system. For example, social security numbers should contain only numeric data. If the data contains letters, such as xxx-xx-xxxx, the data could be considered to have interpretability problems.
    • Recency—The condition measures whether the data is up to date. For example, if an individual moves but the address you have in your system contains the person's old address, the data could be considered to have a recency problem.
    • Uncategorized—Choose this option if you do not want to categorize this condition.
    • Uniqueness—The condition measures whether there is duplicate data. If the dataflow could not consolidate duplicate data, the records could be considered to be an exception.

You must add at least one expression. For more information, see Add or Modify Expression.