Apply pass conditions to custom rules

Data Integrity Suite
Product
Spatial_Analytics
Data_Integration
Data_Enrichment
Data_Governance
Precisely_Data_Integrity_Suite
geo_addressing_1
Data_Observability
Data_Quality
dis_core_foundation
Services
Spatial Analytics
Data Integration
Data Enrichment
Data Governance
Geo Addressing
Data Observability
Data Quality
Core Foundation
ft:title
Data Integrity Suite
ft:locale
en-US
PublicationType
pt_product_guide
copyrightfirst
2000
copyrightlast
2025

Pass conditions define the specific criteria that must be met for the rule to be considered satisfied. These clearly outline the acceptable values using basic and logical operators for complex definitions. Pass conditions can be applied to the rules to evaluate the data based on the defined conditions.

You can choose to apply the pass condition between Basic and Advanced conditions.
  • Basic conditions can be used for simple evaluation scenarios. You can configure a basic condition by selecting the value and the associated operator. The initial value field acts as an alias for all the target fields. Any condition configured in the Basic tab is converted into an expression in the advanced tab.
  • Advanced conditions can be used for more complex evaluation scenarios. Within the Advanced tab, you can configure an expression using numerical, string and logical operators.
    Note: If the pass condition is being applied to a dataset, it is required to map all the fields first post which the rule can be created. A rule cannot be saved without all the fields being mapped.

Pass condition for fields

Pass conditions can be applied to the fields to evaluate the data based on the defined conditions.

Basic condition

Basic condition is used when you want to perform data evaluation based on simple conditions.
  • Select the combination of <attribute><operator><enter value> <value> and type a value that should match the condition.
  • Select the combination of <attribute><operator><select field > <field> and select a field that should match the condition. In this case, you can compare the selected field with field of same data type. You can use this for all available data types.
Note: The value alias condition can be retained in the pass conditions along with the configured parameters. Value alias is used to collectively refer to multiple fields under a single label, simplifying the process of selecting the fields.

The available operators depend on the data type of the selected field.

For example if a boolean datatype is selected, then the third field would provide an option to choose between true or false and if a string datatype is selected, the user would be prompted to enter a specific value.

Note: The sibling fields of the reference dataset are available when defining the pass conditions. When comparing the fields in the pass conditions across datasets, only those fields of the same datatype are available to be selected in the pass conditions.

Pass condition for datasets

Pass conditions can be applied to the selected datasets to evaluate the data based on the defined conditions.
Note: Only the fields that are a part of the reference dataset are displayed while creating the pass conditions.
Basic condition
Basic condition is used when you want to perform data evaluation based on simple conditions.
  1. Select the combination of <attribute><operator><enter value><value> and type a value that should match the condition.
  2. Select the combination of <attribute><operator><select field><field> and select a field that should match the condition. In this case, you can compare the selected field with field of same data type. You can use this for all available data types.

Supported operators

Numeric and temporal field operators

Use numeric operators to evaluate values in Integer, Long, Float, Date, DateTime, and Time fields.

Operator Binary/Unary Expression true when
is Binary Field value is equal to a literal or field value.
is not Binary Field value is not equal to a literal or field value.
is null Unary Field value is null.
is not null Unary Field value is not null.
Greater than Binary Field value is greater than a literal or field value.
Greater than or equal Binary Field value is greater than or equal to a literal or field value.
Less than Binary Field value is less than a literal or field value.
Less than or equal Binary Field value is less than or equal to a literal or field value.

String field operators

Use string operators to evaluate values in textual fields.

Operator Binary/Unary Expression true when
Equal Binary Field string value matches a literal or field value.
Not equal Binary Field string value does not match a literal or field value.
In Binary Field string value matches any one of two or more literal values. (Type and press Enter to add each literal value to the box.)

Logical field operators

Use logical operators to check values in fields that represent either true or false conditions.

Operator Binary/Unary Expression true when
Equal Binary Field value is equal either to a literal True or False value or to the value in another Boolean field.
Null Unary Field value is null.
Not null Unary Field value is not null.

Advanced condition for fields and datasets

Advanced condition is used for complex evaluation operations. You can type the expression using the available operators and functions. The advanced condition is also called as "Expression Builder" since you can build your own complex expressions. The syntax is color coded for better understanding while typing expression. An error is flagged when you mistype or misuse an operator or a function.

Important:
  • When using advanced conditions, it is mandatory to use left and right operands along with the operator.
  • Field names are case sensitive while using advanced conditions. Incorrect field name casing does not flag an error in the expression and will not apply the set condition.

    For example, in ucase(FirstName)=true, the condition is applied when you have FirstName field in your dataset. In ucase(FIRSTNAME)=true, the condition is not applied if your dataset does not have FIRSTNAME field and no error is flagged in the expression.

Operators and their description

Operator Description
= (equals)

Selects records where a field's value is equal to a specified value. For example, Age = '30' selects records where the age is 30.
!= (not equal to)

Selects records where a field's value is not equal to a specified value.
> (greater than)

Selects records where a field's value is greater than a specified value. For example, Price > '50' selects records with prices higher than 50.
< (less than)

Selects records where a field's value is less than a specified value.
>= (greater than or equal to)

Selects records where a field's value is greater than or equal to a specified value.
<= (less than or equal to)

Selects records where a field's value is less than or equal to a specified value.
IN

Selects records where a field's value matches any value in a specified list. For example, City IN ['New York', 'Los Angeles'] selects records with cities matching either "New York" or "Los Angeles."
IS NULL Selects records where the specified field has a null or undefined value. For example, Phone Number IS NULL, if you have a database of customer information and you want to find all records where the "Phone Number" field is not filled in, you would use the "IS NULL" condition on the "Phone Number" field. Phone Number IS NULL
IS NOT NULL Selects records where the specified field has a defined (non-null) value. For example, Email IS NOT NULL, if you want to find all records in a dataset where the "Email" field has been filled in (i.e., it is not null), you would use the "IS NOT NULL" condition on the "Email" field.
AND

Logical operator that combines multiple conditions and returns results that satisfy all of them. For example, you may select data where Age > '25' AND Salary > '50000'.
OR

Logical operator that combines conditions and returns results that satisfy at least one of them. For example, you may select data where Category = 'Electronics' OR Price < '100'.
!

Logical operator that negates a condition, returning results that do not meet the specified criteria. For example, State != 'New York', you may select data where you do not have the "State" as "New York".

Functions and their description

Function Description Usage Example
ucase

Selects a string with uppercase (capital) letters.

Standardize the case of characters in a text field.

 FIRSTNAME=ucase('AMY')
lcase Selects a string with lowercase (small) letters.

Standardize the case of characters in a text field.

 LASTNAME=lcase('stamp')
tcase Selects a string with title case or with uppercase of the first letter of each word.

Update the text in a string to title case.

 tcase('Mrs Mary Smith')=Name
trim Removes leading and trailing spaces from a string. Clean up data, especially when dealing with user-inputted text that may have unnecessary spaces. trim('routley')=LastName
ltrim Removes leading (left-side) spaces from a string. Clean data, ensuring consistent formatting by eliminating leading spaces. ltrim('AMY')=FirstName
rtrim Removes trailing (right-side) spaces from a string. Standardize data by eliminating trailing spaces that might affect comparisons or visual representation. rtrim('stamp')=LastName
lpack Left-packs a string, removing leading spaces and adjusting remaining characters to the left. Align text to the left while eliminating unnecessary leading spaces, aiding in data formatting. lpack('AMY')=FirstName
rpack Right-packs a string, removing trailing spaces and adjusting remaining characters to the right. Align text to the right, removing trailing spaces for consistent formatting. rpack('stamp')=LastName
mask Generates a pattern to an actual input value, replacing certain characters with specified placeholders. Helps verify the datatypes present in the generated pattern. For example, if the pattern generated for the input is AAANN, this indicates that A in the pattern contains alphabets and N represents numeric values. mask(Country)='AAANN'
substr

Extracts a substring from a given string based on specified starting position and length.

Extract specific portions of text from a longer string. Two ways of building expression: substr(string data, int beginIndex) and substr(string data, int beginIndex, int endIndex)

 FIRSTNAME=substr('TAmy',2)

FIRSTNAME=substr('TAmyT',2,5)

concat Joins two or more strings without space. Combine information from multiple fields or sources into a single, cohesive string. concat('AMY',' ')=FirstName
regexp Applies a regular expression pattern to a string for pattern matching. Complex pattern recognition and extraction in textual data. regexp(Email,'@.')='@g'
replace Replaces occurrences of a specified substring or pattern in a string with another substring. Clean and update data, replacing specific elements with desired values. replace('Mircosoft','rco','cro')=Company
split Splits a string into an array of substrings based on a specified separator character. Separate of text data into individual components for analysis or processing. split('Sweden:BasicCountry',':',0)=Country
dt_now Returns current date-time in given format. Ensures that the date-time is displayed in the specified format. dt_now('MM/dd/yyyy hh:mm:ss')
dt_add Increments or decrements the given part of date or time. Modifies the selected date or time associated with the database. dt_add('03/12/2013', 'dd/MM/yyyy', 'Month', 3)
length

Selects a string having the specified length (number of characters).

Know the size of a string, often used for validation.

 length(CollectionNumber) > 1
to_number Selects a numeric string having a specified numerical value. Transform string representations of numbers into numeric formats for mathematical operations. to_number('100')=CustomerId
to_int Selects double value as per specified integer value. Maintain data consistency and facilitate numerical calculations. to_int(22.53)=Currency
occstr Returns the position of the first occurrence of a specified substring in a string. Locate specific substrings within text data. occstr(Email,'.','L')=3
now Selects the current date and time. Provide a dynamic timestamp for real-time data capture or time-sensitive operations. now()=Totaltime
date_to_seconds Converts iso format string to UTC seconds. Standardizes the date in UTC format. date_to_seconds('2023-08-20T12:10:20')
dt_diff Calculate difference b/w two different or same format types of date-time values. Provides an option to calculate the difference between two date formats and returns the value. dt_diff('03/12/2013', 'dd/MM/yyyy', '2014-11-23', 'yyyy-MM-dd')
dt_part Extracts the specified part of date-time and return the value as an integer. Returns the specified parameter in the date time format as specified in the expression. dt_part('2014-02-08', 'yyyy-MM-dd', 'month')
isalpha Selects a string containing only alphabetic characters. Verify if a text field consists solely of letters. isalpha('test')
isnumeric Selects a string containing only numeric characters. Facilitate validation of numeric data integrity in text fields. isnumeric('100')!=CustomerId
isnull Selects fields with the absence of any value. Check fields for missing or unknown data. isnull(NAME)
isempty Selects an empty string. Check that text fields are not null or empty. isempty('')!=PostalCode
contains

Selects a string containing a specific substring.

Search for a particular set of characters within a larger string.

 contains('@', Email) != 0
dt_is_date Checks whether given input is of date or not. Ensures that the data is in the specified date format. dt_is_date('03/12/2013','dd/MM/yyyy')
begins_with

Checks whether a string starts with the value given or not.

 Used to determine if a given string starts with a specified substring. It typically returns a boolean value (true or false) indicating whether the condition is met. begins_with(String data, String value)

begins_with('Test Engineer','Test') returns true.

begins_with ('Test Engineer','Best') returns false.

ends_with Checks whether a string ends with the value given or not. Used to determine whether a given string concludes with a specified suffix or substring. It typically returns a Boolean value (true or false) indicating the result of this check.

ends_with(String data , String stringNeedToSearch)

ends_with('Hello I am from India', 'India') returns true.

ends_with('Hello I am from India', 'from') returns false.

not_in

 It excludes the values present in data. It is used in programming and database queries to exclude values from a specified list. It checks whether a value does not exist in a given set.

not_in(String value, String[] data)

not_in('Ravi',['Manish','Deepu','Meenu','Ravi']) returns false.

not_in ('Ravi',['Manish','Deepu','Meenu']) returns true.

to_date Selects the specified ISO format date (YYYY-MM-DD). Transform date representations in string format for date-related operations. to_date('1973-06-05') = Date
to_time Selects the specified ISO format time (HH:MM:SS). Transform time representations in string format for time-related calculations. to_time('09:04:43')=Time
to_datetime Selects the specified ISO format datetime (YYYY-MM-DDTHH:MM:SS). Integrate date and time information from string formats into datetime values for comprehensive temporal analysis. to_datetime('1973-06-05T09:04:43')=DateTime

Dimensions supported in Custom Rules:

Dimension Description
Completeness Checks for completeness in the configured custom rule
Validity Checks if the entered values are valid thereby eliminating any void or null values.
Consistency Checks for uniformity of information across systems
Accuracy Reflects the degree to which information reflects correctly
Timeliness Ensures the information is available when required
Uniqueness Ensures each data element appears only once in the database.
Note: These dimensions are only used for tagging which helps in easy identification of the dimension based on which the rule is evaluated. These dimensions will not impact the rule evaluation.