Data Validation

Overview

The Fusion Registry is able to validate datasets for which there is a Dataflow present in the Registry.

Data Validation is split into 3 high level validation process:

1. Syntax Validation - is the syntax of the dataset correct
2. Duplicates - format agnostic process of rolling up duplicate series and obs
3. Syntax Agnostic Validation - does the dataset contain the correct content

Data Validation can either be performed via the web User Interface of the Fusion Registry, or by POSTing data directly to the Fusion Registries' data validation web service.

Syntax Validation

Syntax Validation refers to validaiton of the reported dataset in terms of the file syntax. If the dataset is in SDMX-ML then this will ensure the XML is formatted correctly, and the XML Elements and XML Attributes are as expected. If the dataset is in Excel Format (propriatory to the Fusion Registry) then these checks will ensure the data complies with the expected Excel format.

Duplicates Validation

Part of the validation process is the consolidation of a dataset. Consolidation refers to ensuring any duplicate series are 'rolled up' into a single series. This process is important for data formats such as SDMX-EDI, where the series and observation attributes are reported at the end of a dataset, after all the observation values have been reported.

Example: Input Dataset Unconsolidated

Frequency	Reference Area	Indicator	Time	Observation Value	Observation Note
A	UK	IND_1	2009	12.2	-
A	UK	IND_1	2010	13.2	-
A	UK	IND_1	2009	-	A Note

After Consolidation:

Frequency	Reference Area	Indicator	Time	Observation Value	Observation Note
A	UK	IND_1	2009	12.2	A Note
A	UK	IND_1	2010	13.2	-

The above consolidation process does not report the duplicate as an error, as the duplicate is not reporting contradictory information, it is supplying extra information. If the dataset were to contain two series with contradictory observation values, or attributes, then this would be reported as a duplication error

Example: Duplicate error for the observation value reported for 2009

Frequency	Reference Area	Indicator	Time	Observation Value	Observation Note
A	UK	IND_1	2009	12.2	-
A	UK	IND_1	2010	13.2	-
A	UK	IND_1	2009	12.3	A Note

Syntax Agnostic Validation

Syntax Agnostic Validation is where the majority of the data validation process happens. Like the name suggests, the validation is syntax agnostic, and therefore the same validation rules and processes are applied to all datasets, regardless of the format the data was uploaded in.

This validation process makes use of a single Validation Manager and multiple Validation Engines. The validation manager walks the contents of the dataset (Series and Observations) in a streaming fasion, and as each new Series or Observation is read in, it asks the same question to each registered Validation Engine - the question is "is this valid?".

An conceptual example of the Validation Manager delegating validation questions to each Validation Engine in turn

The purpose of a Validation Engine is to perform ONE type of validation, this allows configuration of each validation engine as a seperate entity, and new validation engines can be easily added to the product if there is a new type of validation rule to implement. Validation Engines can be switched off, or have a different level of error reporting set, validation engines can also have a error limit set, so that a single engine can be decommisioned from validating a particular dataset if it is reporting too many errors. In the validation report that is produced, the errors are grouped per validation engine.

The following table shows each validation engine and its purpose

Validation Type	Validation Description
Structure	Ensures the Dataset reports all Dimensions and does not include any additional Dimensions or Attributes
Representation	Ensures the reported values for Dimensions, Attributes, and Observation values comply with the DSD
Mandatory Attributes	Ensures all Attributes, as defined in the Data Structure Definition (DSD), are reported if they are marked as Mandatory
Constraints	Ensures the reported values have not been disallowed due to Content Constraint definitions
Mathematical Rules	Performs any mathematical calculations, defined in Validation Schemes, to ensure compliance
Frequency Match	Ensure the reported Frequency code matches the reported time period Example: FREQ=A will expect time periods in format YYYY
Obs Status Match	Ensure observation values are in keeping with the Observation Status Example: Missing Value, does not expect a value to be reported
Missing Time Period	Ensures the Series has no holes in the reported time periods

Security

Data Validation is by default a public service and as such a user can perform data validation with no authentication required. It is possible to change the security level in the Registry to either:

• Require that a user is authenticated before they can perform ANY data validation
• Require that a user is authenticated before they can perform data validation on a dataset obtained from a URL