Data Action - Revision history

Dgreenwood: /* Data Rules */

2025-07-29T17:27:05Z

Data Rules

← Older revision		Revision as of 12:27, 29 July 2025
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	=== Data Rules ===		=== Data Rules ===

	When a Data Rule is executed ~~(such as by the [[Apply Rules]] activity)~~, its Data Actions are applied to the document’s data instance. The process is as follows:		When a Data Rule is executed, its Data Actions are applied to the document’s data instance. The process is as follows:

	# The Data Rule evaluates its trigger condition.		# The Data Rule evaluates its trigger condition.
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	This approach is ideal for normalization, validation, and enforcing business logic after extraction.		This approach is ideal for normalization, validation, and enforcing business logic after extraction.


			Data Rules can be executed in several Grooper activities:

			* '''[[Apply Rules]]''': The Apply Rules activity is designed specifically to apply one or more Data Rules to each document in a batch or folder. If any Data Rule raises issues, the document can be flagged.
			* '''[[Extract]]''': The Extract activity may optionally execute a Data Rule in two ways:
			** The "Rules" property on the Extract activity allows you to specify a list of Data Rules to be applied after extraction.
			** A Data Model, Data Section and/or Data Table will execute a Data Rule when its "Validate Rule" property is configured.
			* '''[[Convert Data]]''': The Convert Data activity can apply Data Rules as preprocessing (before conversion) or postprocessing (after conversion) steps. This is useful for cleaning or validating data before or after it is mapped to a new content type.
			* '''[[Route]]''': The Route activity can use Data Actions (including the "Execute Rule" action to apply Data Rules) to transform or map data when routing documents.
			* '''[[Review]]''': Data Rules may be manually executed by a user in a Review step in the Data Viewer.

	=== Convert Data and Route Activities ===		=== Convert Data and Route Activities ===

Dgreenwood: /* Data Actions Overview */

2025-07-29T17:08:54Z

Data Actions Overview

← Older revision		Revision as of 12:08, 29 July 2025
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	Grooper provides a variety of built-in Data Actions. Each Data Action can be configured with properties such as "Display Name" and "Remarks" for documentation and clarity.		Grooper provides a variety of built-in Data Actions. Each Data Action can be configured with properties such as "Display Name" and "Remarks" for documentation and clarity.

	~~= Data Actions Overview =~~		Below is a summary of each available Data Action.

	Data Actions are modular operations used within [[Data Rule]]s to automate, validate, and manipulate data in your extraction process. Each Data Action targets specific data elements, fields, or collections, enabling flexible and reusable logic for a wide range of scenarios. Below is a summary of each available Data Action.

	=== Calculate Value ===		=== Calculate Value ===

Dgreenwood: /* Types of Data Actions */

2025-07-29T17:07:52Z

Types of Data Actions

@@ Line 70: / Line 70: @@
 == Types of Data Actions ==
-Grooper provides a variety of built-in Data Actions, including:
+Grooper provides a variety of built-in Data Actions. Each Data Action can be configured with properties such as "Display Name" and "Remarks" for documentation and clarity.
-* '''Set Field Value:''' Assigns or calculates a value for a field.
+= Data Actions Overview =
-Each Data Action can be configured with properties such as "Display Name" and "Remarks" for documentation and clarity.
+Data Actions are modular operations used within [[Data Rule]]s to automate, validate, and manipulate data in your extraction process. Each Data Action targets specific data elements, fields, or collections, enabling flexible and reusable logic for a wide range of scenarios. Below is a summary of each available Data Action.
 == Best Practices ==

Dgreenwood: Created page with "'''Data Actions''' are a core concept in Grooper’s data processing framework, enabling powerful, reusable, and configurable operations for manipulating, transforming, and validating extracted document data. They are the building blocks for data normalization, business rule enforcement, and data model conversion throughout Grooper’s workflow. == What Are Data Actions? == A '''Data Action''' is a discrete operation that acts on document data. Data Actions can: * Ass..."

2025-07-29T16:25:12Z

Created page with "'''Data Actions''' are a core concept in Grooper’s data processing framework, enabling powerful, reusable, and configurable operations for manipulating, transforming, and validating extracted document data. They are the building blocks for data normalization, business rule enforcement, and data model conversion throughout Grooper’s workflow. == What Are Data Actions? == A '''Data Action''' is a discrete operation that acts on document data. Data Actions can: * Ass..."

New page

'''Data Actions''' are a core concept in Grooper’s data processing framework, enabling powerful, reusable, and configurable operations for manipulating, transforming, and validating extracted document data. They are the building blocks for data normalization, business rule enforcement, and data model conversion throughout Grooper’s workflow.

== What Are Data Actions? ==

A '''Data Action''' is a discrete operation that acts on document data. Data Actions can:

* Assign or transform field values
* Parse and distribute values to other fields
* Copy or move data between elements
* Perform lookups and validations
* Execute custom logic as part of a data processing pipeline

Data Actions are designed to be modular and reusable. With [[Data Rule]]s, they can be combined into hierarchies allowing complex, multi-step data transformation and validation logic to be built from simple, focused operations.

== Why Use Data Actions? ==

Data Actions provide a clear separation between data extraction (locating data in documents) and data normalization or transformation (making data fit business requirements). This separation offers several benefits:

* '''Simplified Extraction:''' Keep extractors focused on finding data, not transforming it.
* '''Reusability:''' Common transformations (such as splitting a full name or normalizing phone numbers) can be reused across multiple rules and workflows.
* '''Maintainability:''' Changes to business logic or data mapping can be made in one place, without modifying extraction logic.
* '''Advanced Scenarios:''' Enable complex data mapping, conditional logic, and multi-stage processing.

== Where Are Data Actions Used? ==

Data Actions are used in several key areas of Grooper:

=== 1. Data Rules ===

A '''Data Rule''' defines logic to be applied to document data, such as normalization, validation, or business rule enforcement. Each Data Rule can have one or more Data Actions assigned to its "True Action" or "False Action" properties. When a Data Rule is executed (for example, by the [[Apply Rules]] activity), its Data Actions are applied to the relevant data instances.

* '''Example:''' A Data Rule might check if a "Full Name" field is populated. If so, a Data Action can split it into "First Name" and "Last Name" fields.

=== 2. Activities: Convert Data and Route ===

Some Grooper activities use Data Actions to perform data mapping and transformation as part of broader workflow steps:

; '''Convert Data'''
: The [[Convert Data]] activity transforms a document from one [[Content Type]] (data model) to another. The "Actions" property is a collection of Data Actions that define how data is copied, mapped, or transformed from the source to the target type. This is essential for data normalization, especially when consolidating data from multiple document types into a single export format.

; '''Route'''
: The [[Route]] activity can move or clone documents into different batch processes based on classification or business rules. When routing between different Content Types, Data Actions can be defined to map, copy, or transform data fields as part of the routing process, ensuring that data fits the requirements of the target workflow.

== How Data Actions Are Applied ==

=== Data Rules ===

When a Data Rule is executed (such as by the [[Apply Rules]] activity), its Data Actions are applied to the document’s data instance. The process is as follows:

# The Data Rule evaluates its trigger condition.
# If the condition is met, the "True Action" (a Data Action or hierarchy of Data Actions) is executed.
# If the condition is not met and a "False Action" is defined, it is executed instead.
# Each Data Action manipulates the data as configured—assigning values, performing calculations, or raising issues.

This approach is ideal for normalization, validation, and enforcing business logic after extraction.

=== Convert Data and Route Activities ===

In activities like [[Convert Data]] and [[Route]], Data Actions are used to map and transform data between different Content Types:

* In [[Convert Data]], the "Actions" collection specifies the sequence of Data Actions to execute. These actions define how each field or group of fields is copied, transformed, or calculated in the target data model.
* In [[Route]], Data Actions can be part of the routing definition, ensuring that when a document is moved or cloned into a new process (potentially with a different Content Type), its data is mapped and transformed appropriately.

This enables advanced scenarios such as:

* Data normalization from multiple source types to a single output type
* Conditional data mapping based on business rules
* Multi-stage processing where data is transformed at each step

== Types of Data Actions ==

Grooper provides a variety of built-in Data Actions, including:

* '''Set Field Value:''' Assigns or calculates a value for a field.
* '''Copy Base:''' Copies groups of fields or hierarchical data.
* '''Parse Value:''' Splits or parses a field into multiple outputs.
* '''Raise Issue:''' Flags data issues for review.
* And many more, including custom actions for advanced scenarios.

Each Data Action can be configured with properties such as "Display Name" and "Remarks" for documentation and clarity.

== Best Practices ==

* Use Data Actions to keep extraction logic simple and focused.
* Group related Data Actions into Data Rules for maintainability.
* Use the "Actions" collection in [[Convert Data]] to centralize data mapping and transformation.
* Document your Data Actions with clear display names and remarks.
* Test Data Actions in isolation before integrating them into larger workflows.

== Summary Table: Data Actions in Context ==

{| class="wikitable"
! Context !! How Data Actions Are Used !! Typical Use Cases
|-
| [[Data Rule]]s (e.g., via [[Apply Rules]]) || Applied as "True Action" or "False Action" for normalization, validation, or business logic || Splitting fields, enforcing required values, raising issues
|-
| [[Convert Data]] activity || "Actions" collection defines mapping/transformation from source to target Content Type || Data normalization, output formatting, multi-stage processing
|-
| [[Route]] activity || Data Actions map/transform data when routing between Content Types || Workflow branching, exception handling, parallel processing
|}

== See Also ==

* [[Data Rule]]
* [[Apply Rules]]
* [[Convert Data]]
* [[Route]]
* [[Content Type]]
* [[Batch Folder]]