Docs

Data Quality Feature

# Introduction

The Data Quality Feature enables administrators to evaluate and correct data records. This feature is based on two parallel concepts:

  • Data Check Rules – identify data inconsistencies using queries and optional conditions
  • Transformation Steps – automatically or manually correct values in records

Both mechanisms share a common structure and can be triggered manually.

# Core Entities and Relationships

# Data Check Rules

Display Name Logical Name Description
Data Check Rule talxis_datacheckrule Defines a validation rule using a FetchXML filter and optional PowerFx condition.
Data Check Log talxis_datachecklog Result log of a check rule execution on a specific record.

# Transformation Steps

Display Name Logical Name Description
Data Transformation Step talxis_datatransformationstep Defines a correction rule, including FetchXML query, attribute to update, and logic.
Data Transformation Log talxis_datatransformationlog Result log containing the original and new value.

# Shared Relationships

Both talxis_datachecklog and talxis_datatransformationlog include a polymorphic lookup:

Field Description
talxis_datarecordid Reference to the target record affected by the rule (e.g., document header or line).

# Execution Flow

# Automatic Execution (Albert VAT App only)

After records are imported (e.g. via Dataflows or Power Automate Flows), the main orchestration logic runs in the following sequence:

  1. Pre-transformation data adjustments (e.g. categorization, enrichment)
  2. All matching Transformation Steps (filtered by entity)
  3. All matching Data Check Rules (filtered by entity)

This ensures records are corrected before checks are run, and issues are flagged only if still unresolved.

⚠️ This applies only to Albert VAT App. See User Guide (opens new window) to get more info.

# Manual Execution

Both check rules and transformation steps can be manually triggered from the admin section. The buttons "Execute Rule" and "Execute Step" are available in the grid of the respective entity.

# Manual Corrections

Users can also manually edit records using a dedicated dialog. This action:

  • Lets the user select the field and input a new value
  • Creates a transformation log with type Manual
  • Applies the change using plugin logic (ResolveDataTransformationLog)

# Filtering and Expressions

# FetchXML Filtering

Each rule/step includes a FetchXML query to identify relevant records. Complex logic can be applied using related entities and groups.

# Conditions via PowerFx (Functions)

To express more advanced conditions (e.g. math, combined fields), custom PowerFx-based APIs are used:

  • Written as low-code plug-ins
  • Require input RecordId and output Result (boolean)

Example syntax:

{
  Result:
    LookUp('Purchase Invoice Lines','Line ID' = GUID(RecordId)).Code = "0"
}
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# Transformation Logic via PowerFx

For automated corrections, PowerFx functions can return a NewValue string to be applied to the target field.

Example:

{
  NewValue:
    Text(
      Patch(
        'Purchase Invoice Lines',
        LookUp('Purchase Invoice Lines', 'Line ID' = GUID(RecordId)),
        { Code: "XX"}
      ).Code
    )
}
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# Status and Logs

# Check Log Statuses (talxis_datachecklog.statuscode)

Value Meaning
742070000 Evaluating condition
742070001 Awaiting review
742070002 Checked (user confirmed/corrected)
742070003 Cancelled (condition false)
742070004 Failed (custom API error)

# Transformation Log Statuses (talxis_datatransformationlog.statuscode)

Value Meaning
742070000 Evaluating condition
742070001 Awaiting processing
742070002 Processed
742070003 Cancelled (condition false)
742070004 Failed (custom API or plugin error)

# Plugins

  • ValidateCustomApiCondition – evaluates PowerFx condition and updates status
  • ResolveDataTransformationLog – applies new value based on log record and updates status

# Power Automate Flows

  • talxis_datalog_creatorhandler – creates transformation/check logs in batches
  • talxis_waitforpreviousinstancesandlogsfinished – ensures steps run in defined order
  • talxis_executedatacheckrule_trigger – triggers evaluation of a Data Check Rule
  • talxis_executedatatransformationstep_trigger – triggers execution of a Data Transformation Step

# Summary

The Data Quality Feature enables structured validation and correction of records. It supports both automated (see VAT APP User Guide (opens new window) for example) and manual workflows and provides a transparent, traceable mechanism to ensure data accuracy prior to downstream processing.

Checks