Home
Data Quality
Assure reliability, consistency, and accuracy data from different sources to deliver value to employees and customers.
Data Quality pipelines
Pipelines define and apply transforms to data loaded by a dataset.
Reference
This section contains reference information for pipeline pages in Precisely DQ.
Data Quality Transforms
Pipeline transform settings are configured to add transformation steps in a pipeline. This reference describes the purpose of each transform and its settings that define a transformation step in a pipeline.
Execute Formula
Performs operations on values and inserts the result in a new column.
Execute Formula operators

Welcome
The Precisely Data Integrity Suite is designed to deliver trusted data with accuracy, consistency, and context – built on the core capabilities of data integration, data quality, data governance, location intelligence, and data enrichment.
Data Integration
Replicate data from mainframe, IBM i, and relational databases to cloud analytics platforms.
Data Observability
Proactively monitor and manage the health of your data. Receive actionable insights through alerts that drive business decisions.
Data Governance
Manage data policy and processes with greater insight into your data's meaning, lineage, and impact.
Data Quality
Assure reliability, consistency, and accuracy data from different sources to deliver value to employees and customers.
- Data Quality Catalogs
  The Data Integrity Suite Data Catalog lets users discover and understand connected data sources.
- Data Quality datasets
  A dataset defines a data source and characterizes data to be loaded and transformed by pipelines.
- Data Quality pipelines
  Pipelines define and apply transforms to data loaded by a dataset.
  - About Data Quality pipelines
  - Using Data Quality pipelines
  - How to
  - Reference
    This section contains reference information for pipeline pages in Precisely DQ.
    - Pipelines
      The table on this page lists pipelines. On this page you can create a new pipeline or edit, delete, or rename any existing pipeline.
    - Pipeline settings
      The pipeline settings page allows you to view and edit transformation steps in a pipeline.
    - Add Step
      Choose a transformation in this dialog box to add it as a step in a pipeline.
    - Data Quality Transforms
      Pipeline transform settings are configured to add transformation steps in a pipeline. This reference describes the purpose of each transform and its settings that define a transformation step in a pipeline.
      - Case Field
        Converts strings in a field into upper, lower, or title case.
      - Copy Column
        Copies a column to one or more new columns.
      - Execute Formula
        Performs operations on values and inserts the result in a new column.
        Execute Formula operators
        Execute Formula order of operations
        Execute Formula functions (by category)
        List of functions available in the Execute Formula transform.
        Execute Formula functions (alphabetical)
        List of functions available in the Execute Formula transform.
      - Filter Column
        Removes selected columns from a table.
      - Get Substring
        Returns the substring from the start position to the end position. Index of the first character is 1.
      - Pad String
        Pads the beginning or end of field content with a string to a specified length. The Pad field transform does not change the value of the original string.
      - Parse Name
        This transformation parses personal and business names into their constituent parts, such as given name, surname, and titles and qualifiers.
      - Rename Column
        Renames a column in a pipeline.
      - Replace Between
        Replaces a substring between two specified characters.
      - Replace By Position
        Replaces the substring from the start position to the end position.
      - Replace Consecutive Spaces
        Replaces all instances of two or more consecutive spaces in a string field value with a single space.
      - Filter Row
        The row filter evaluates whether to include rows depending on a logical expression that evaluates to either true or false.
      - Search and Replace
        Use Search and Replace in a pipeline to search for strings in columns and replace them with a different string.
      - Split Column
        Splits a column into two new columns.
      - Standardize Date
        If a data column contains dates in different formats, use the Standardize Date transformation to convert values in all records to a single format.
      - Standardize Field
        The Standardize Field transformation applies one or more standard conversions to semantically typed fields.
      - Trim String
        Removes specified characters from the start or end of a string.
      - Verify Address
        The Verify Address transform parses, matches, and formats address data against reference postal data. A confidence level is assigned to each verified record.
- Data Quality jobs
- Data Connections
  Connecting to data is the first step in processing, analyzing, and storing information.
Administration
Manage your subscription, your users and their roles, and other system tasks.

Execute Formula operators

There are four different types of calculation operators: arithmetic, comparison, text concatenation, and reference.

Arithmetic operators

To perform basic mathematical operations on numeric values, such as addition, subtraction, or multiplication; combine numbers; and produce numeric results, use the following arithmetic operators.

Arithmetic operator	Meaning	Example
`+` (plus sign)	Addition	`3+3`
`-` (minus sign)	Subtraction Negation	`3-1` `-1`
`*` (asterisk)	Multiplication	`3*3`
`/` (forward slash)	Division	`3/3`
`%` (percent sign)	Percent	`20%`
`^` (caret)	Exponentiation	`3^2`

Comparison operators

You can compare values with the following operators. When two values are compared by using these operators, the result is either 1 (True) or 0 (False).

Comparison operator	Meaning	Example
`=` (equal sign)	Equal to	`[Col1]=[Col2]`
`>` (greater than sign)	Greater than	`[Col1]>[Col2]`
`<` (less than sign)	Less than	`[Col1]<[Col2]`
`>=` (greater than or equal to sign)	Division	`[Col1]>=[Col2]`
`<=` (less than or equal to sign)	Less than or equal to	`[Col1]<=[Col2]`
`<>` (not equal to sign)	Not equal to	`[Col1]<>[Col2]`

Text concatenation operator

Use the ampersand (&) to join, or concatenate, one or more text strings to produce a single piece of text.

Reference operator	Meaning	Example
`&` (ampersand)	Connects, or concatenates, two values to produce one continuous text value.	`IF ([n1] > 2, [n1] & " is bigger than 2", [n1] & " is less than or equal to 2")`

Reference operators

Combine ranges of column cells in a row for calculations with the following operators.

Reference operator	Meaning	Example
`:` (colon)	Range operator, which combines multiple references into one argument. Reference every column that you want included in an argument.	`LARGE([Col1]:[Col2]:[Col3]:[Col4],1)`
`,` (comma)	Union delimiter, which combines multiple references.	`SUM([Col1],[Col5],[Col7],[Col9])`
(space)	Intersection operator, which produces a reference to columns common to the two references.	`LARGE([Col1]:[Col2]:[Col3]:[Col5] [Col3]:[Col4]:[Col5]:[Col6]:[Col7],1)`
`#`	The # symbol is used as part of an error name.	`#NULL!`