Automated Validation Checks

To guarantee the reliability of calculated emissions, Normative utilizes a robust, two-tier automated data validation process.

This system acts as a strict internal control, systematically filtering out formatting anomalies, omissions, and data corruption before data can be processed. This article outlines the automated checks performed by the Normative engine, to give a clear overview of the systemic errors that are eliminated during the upload flow.

The Two-Tier Validation Architecture

Normative’s validation framework operates at both the pre-upload stage and the ingestion stage to ensure zero-fault data processing.

1. 

   Tier 1: In-Template Validation 
   Every GHG category template includes a built-in quality control column on the far right titled Validation Status. As data is compiled, this column evaluates inputs row-by-row against formatting requirements, providing users with immediate feedback on errors.
    

2. Tier 2: Ingestion Gateway 
   The validation system is not merely an advisory tool. When a file is uploaded to the platform, the Normative engine runs these validation protocols programmatically. The engine blocks any rows containing errors from being processed. These faulty rows are isolated and assigned a "Change needed" status on the platform. This ensures that faulty data can never enter the carbon accounting ledger or compromise calculation metrics.

Automated Checks Performed

The Normative engine automatically executes three core categories of validation checks to guarantee data compliance:

1. Completeness Checks (Mandatory Field Verification)

To establish a verifiable audit trail for carbon calculations, certain information and activities are strictly required. The engine checks every row to ensure:

• Mandatory administrative fields, such as Supplier Name, Country Code, Start Date, and End Date must be populated.
• Data Presence: The engine verifies that at least one viable alternative for emission calculation is provided. For example, in Scope 2 Electricity data, a row must contain at least one valid activity metric: Energy Use (Alt 1), Cost of Electricity (Alt 2), Facility Area (Alt 3), or Pre-calculated GHG emissions. If all alternatives are blank, the row is flagged as incomplete.

2. Format & Data Type Constraints

To prevent algorithmic interpretation errors, the engine strictly enforces structural formatting rules:

• Numeric Integrity: Fields reserved for quantitative values (e.g., consumption volumes, financial expenditure, or floor space) reject text strings (e.g., entering "ten thousand" instead of 10000) or non-numeric symbols.
• Temporal Consistency: Dates must conform to the standardized international format (YYYY-MM-DD). This prevents ambiguities regarding time frames and ensures emissions are mapped to the correct reporting period.

3. Standardization & Reference Dictionary Integrity

Normative cross-references input variables against recognized international standard dictionaries to eliminate discrepancies from regional formatting variations or typos:

• Geographical Codes: National data must align precisely with the ISO-3166-1 alpha-2 standard (e.g., SE for Sweden, GB for the United Kingdom).
• Currency Codes: Financial values must map directly to standard ISO-4217 currency codes (e.g., EUR, USD, GBP) to enable accurate inflation and exchange rate adjustments during calculation.
• Nomenclature Alignment: Activity units must match Normative’s recognized unit validation database exactly (e.g., kWh or MWh for energy; m² or sq ft for area). 

Audit Implications: What This Means for Data Assurances

For internal and external auditors, Normative's automated ingestion safeguards provide several critical assurances:

• Elimination of Downstream Processing Errors: Because rows with errors are systematically blocked and marked as "Change needed”, we eliminate the possibility of  invalid formatting, missing fields, or unrecognized units to corrupt the calculations.
• Consistency of Calculations: Enforcing standardized inputs (ISO codes, strict date formats) ensures that the emission factor models applied by the engine map seamlessly and accurately to the source data.
• Data Origin Confidence: To maintain a structured foundation for the carbon accounting audit trail, the system immediately processes all valid rows from an uploaded template. Any invalid entries are automatically rejected, and the file is marked as "change needed" to prompt targeted corrections.