Data Validation Tool | Pricefx Knowledge Base

The Data Validation Tool is a Pricefx capability that lets you validate data provided by customers before that data enters Pricefx processes. The tool analyzes a provided file, reports data issues, and lets you correct the data and rerun the validation so the file is ready for use in downstream workflows such as Data Upload and accelerator deployment.

Navigation: Account > Data Tools > Data Validation Tool

What the Data Validation Tool Is

The Data Validation Tool is available in the Account area of PlatformManager under Data Tools > Data Validation Tool. From the tool landing screen, you select Start new validation to begin. The tool then guides you through a four-step workflow shown at the top of the screen:

Validations setup
Data source setup
Data mapping
Report Overview

The tool detects missing values, duplicates, and rule conflicts and presents the results with reports and fix suggestions.

The Problem It Solves

Customer data delivered for a Pricefx implementation is often inconsistent. Common issues include missing mandatory values, duplicate identifiers, incorrect field types, stray leading or trailing whitespace, empty rows, and references that do not resolve across files.

Without an early validation step, these issues surface later during data upload or accelerator deployment, where they are slower and more expensive to correct. The Data Validation Tool moves data quality checks earlier in the process. Its goal is to accelerate the implementation process and reduce the time to value.

Who Uses the Data Validation Tool

The Data Validation Tool serves two primary audiences:

Implementation teams: Pricefx implementation staff who validate customer-provided data to speed up onboarding.
Partition data uploaders: Users who validate data before uploading it to a partition.

Validation Scenario Types

The Data Validation Tool organizes its checks into validation scenarios. In the Validations setup step, you select Add more scenarios to open the Add validation scenarios dialog and choose the scenarios that apply to your data. The dialog includes a Type to find search field and a list of scenarios with descriptions, confirmed with the Confirm button.

The scenarios fall into three conceptual types.

Accelerator Readiness

Accelerator readiness validates data for a particular accelerator, for example Sales Insights. This type confirms that the data meets the structural and business requirements of the target accelerator. Typical checks include:

Mandatory fields: Confirms that required fields contain values.
Optional fields: Reviews optional fields where relevant.
Field types: Confirms that values match the expected data type.
Cross-file references: Confirms that references resolve across files. For example, the transaction file does not contain a Product ID that is absent from the product file.
Basic calculations: Confirms expected relationships between values. For example, Cost plus Margin equals List Price.
Data cleanliness: Confirms the data is free of common quality issues.

Whether a field is treated as mandatory (for empty-value detection) or as a key (for duplicate detection) is determined per entity from the partition. As a result, an empty value is reported only when it occurs in a field that is mandatory for the target entity; an empty value in a non-mandatory field is intentionally not reported.

Partition Upload Readiness

Partition upload readiness validates files against the existing data structures on the partition. This type confirms that a file matches the target partition before upload. Typical checks include:

Key fields: Confirms that key fields are present and valid.
Field types: Confirms that values match the expected data type.
Data cleanliness: Confirms the data is free of common quality issues.

File Cleanliness Only

File cleanliness only validates the file itself, primarily independent of Pricefx structures. This type is useful when you want to assess a file without committing to a target accelerator. You can optionally select a partition; when a partition entity is linked to an added file, the tool fetches the entity metadata (such as data types and mandatory flags) from the partition, so the scenario is not strictly file-only. Typical checks include:

Data cleanliness: Detects issues such as duplicates, empty rows, and trailing spaces.
Field types: Confirms data types based on the definition you provide.

Validation Scenarios Reference

The Add validation scenarios dialog presents the following named scenarios, which map to the Accelerator Readiness, Partition Upload Readiness, and File Cleanliness Only types. This table is the single reference for the available validation scenarios.

Scenario Name	Description
Data Cleanliness	Ensures key identifier columns do not contain stray leading or trailing whitespace.
Data Cleanliness All Columns	Ensures all columns do not contain stray leading or trailing whitespace.
Comprehensive Data Quality	Single scenario bundling mandatory-field, uniqueness, referential integrity, non-negative and trend checks across customers, products and transactions.
Currency and Unit of Measure	Ensures transaction currency codes belong to a known set of supported ISO codes.
Mandatory Field Validation	Ensures required identifiers and business keys across customers, products and transactions are populated.
Negative Value Checks	Ensures numeric measures that must never be negative (quantities, prices, costs) are non-negative.
Price Trend Validation	Validates the expected ordering of price levels: `net_price` <= `invoice_price` <= `list_price` for each transaction.
Revenue / Price / Quantity Consistency	Validates that `invoicePrice` * `quantity` is always strictly positive whenever both fields are populated and quantity is positive.
Referential Integrity Checks	Ensures every transaction references an existing customer and product in the master data.
Row Count Sanity	Ensures each entity has at least one record and the transaction table has a minimum viable volume for analytics.
Uniqueness Validation	Ensures primary identifiers are unique across master data and transactional data.
Generic Validation	Validates files without requiring an entity type. You can optionally select a partition; when a partition entity (Entity Type and Entity Name) is linked to an added file, the metadata (data types, mandatory flags) is fetched from the partition. For each field you can select the validation to apply (`mandatory`, `uniqueness`, `notNull`).

ISO currency codes are the standardized three-letter codes for currencies, for example EUR and USD.

Data Source Setup and Data Mapping

After you select validation scenarios, the Data source setup step prompts you to provide a file for each required entity or data source. You can upload a new file or reuse a file you uploaded recently. In the Data mapping step, you align the columns in your file with Pricefx columns, set the parsing options (such as separator, quote character, and date format), and review the required and optional mappings, including AI-assisted suggestions.

For the full step-by-step procedure, see How to Run Data Validation Tool.

Usage Modes

The Data Validation Tool supports two usage modes that share the same validation logic.

Standalone mode: You run the tool directly to validate a file independently. The standalone mode supports validation selection, file selection, and data mapping driven by the selected validation types. See How to Run Data Validation Tool.
Accelerator-embedded mode: The validation capability is plugged into accelerators, so validation runs as part of an accelerator workflow. See Data Validation Tool: Accelerator Deployment.

The two modes relate by reusing the same scenarios and checks. Once you have corrected any reported issues, you can reuse the same file in other processes, such as Data Upload or accelerator deployment, by selecting it from your recently uploaded files instead of uploading it again.

Trade-Offs and Considerations

The Data Validation Tool front-loads data quality work. The benefit is fewer failures during upload and deployment. The trade-off is an additional preparation step before data enters a partition. Selecting more scenarios increases coverage but also increases the number of files and mappings you must provide and review.

How to Run Data Validation Tool — Step-by-step procedure for the standalone wizard.
Data Validation Tool: Accelerator Deployment — Using the embedded Data Validation step during accelerator deployment.