Validation rules

Preview: This is a preview feature, and remains a work in progress. The following content may be subject to change before full release. If you have any questions regarding this preview feature, please direct them to our support team.

Validation rules allow you to set conditions for each column in your data, determine whether these conditions are met, and specify actions to take when importing data via the Apteco Customer Data Platform (CDP).

Benefit: Helps you ensure that only clean, accurate data, that meets your standards is imported into the CDP. This prevents problematic records, invalid dates, values larger than a given maximum length etc from interfering with your attainment of consistent CDP data.

Validation rules are only available when importing data into the CDP via data mapping. Once you have your CDP data mapping configured, you can proceed to define validation rules for it.

Configuring validation rules

To configure your validation rules:

  1. Go to your CDP defined data mapping.

  2. Open the Option menu and select Edit.

    You now configure your validation rules on a per column basis.

    Note: The process is essentially the same for each column, but some columns have different default settings. For example, for date of Birth, Data Validation Data Type is only recognised in ISO or UK date formats.

  3. Click the Option menu next to your chosen column and select Validation Rules.

    You’re now presented with a Validation Rules side panel menu, split into the following four collapsible sections:

Data validation

Data validation for all variable types (Currently only validates for date values in CDP V1).

Length validation

Maximum length (with a default setting of 200) and the associated Error Action for values outside of your defined length.

Available Error Actions include:

  • Abandon processing of import: Stop the import completely and don’t process any further records

  • Reject row: Skip the current row but continue processing the rest of the data

  • Set value to Null: Import the row with a null value for this column

  • Warn: Import the data, but raise a warning message

  • No action: Import the data without warnings

  • Truncate to maximum length: Import the data, but only up to the number of characters specified

Allowed values validation

Define a set of values used for a particular column.

To do so, click + New Rule. This presents you with the Allowed Values and Assign Values fields.

This section allows you to standardise your data by defining valid values and optionally remapping variations to a common format.

For example for a title column, you could have allowed values of ‘Mr,mr,Mister,mister’ and a normalised assigned value or ‘Mr’.

You could consolidate inconsistent entries in a ‘Consent’ column (e.g., "True," "true," "Yes," "1") by creating rules that map all positive responses to "Yes" and all negative responses (”False”, “false”, “No”, “0”) to "No”.

Rule 1: Positive case

This rule defines a variety of acceptable values (1,y,Y,Yes,yes,T,t,True,true) and if found will rewrite as Yes.

Rule 2: Negative case


This rule defines a variety of acceptable values (0,n,N,No,no,F,f,False,False,X,x) which if found, will be rewritten as No.

Blocked values validation

Blocked values validation is essentially the inverse of allowed values validation, allowing you to prevent specific values from being imported. You can define rules with multiple values and a single action, or multiple rules with different error actions.

 

Available error actions include:

 

  • No Action: Do not import data, do not notify

  • Abandon Processing of Import: Stop processing import entirely and notify

  • Reject Row: Do not import this row, but continue processing

  • Set Value To Null: Import the row, but without a value for this column

  • Warn Only: Import the row but record a warning message

This feature is useful for data cleansing, validation, and removing unwanted content like offensive words from the input data. Another example might be specific keywords indicating test data etc.

e.g. the following rule rejects the entire record if either keyword “test” or “Test” or “TEST” is found in the input data.

Viewing validation results

Once you’ve finished applying validation rules to your columns, you can import or view your last import to view your import summary with your validation modifications, passes and, fails.

To import your data mapping to CDP or view your last CDP import, click your selected data mapping Option button and select the Import to CDP or View Last CDP Import.

Overview of your validation details:

Drill-down detail of specific validation details: