2. Data Collection and Quality Assurance
Ensuring the accuracy, completeness, and reliability of data is a cornerstone of Emvide's reporting framework. This page outlines the systematic approach to data collection, validation, and quality assurance across all lifecycle assessment (LCA) and Product Carbon Footprint (PCF) studies.
Overview
Emvide employs a robust, AI-powered data collection and validation process to ensure data quality across primary and secondary sources. This process includes automated checks, practitioner oversight, and detailed documentation for transparency.
Data Sources
1. Primary Data
Primary data refers to specific information collected directly from the organisation under assessment. Examples include:
Energy consumption records.
Material input quantities and waste outputs.
Production process details.
Key Methods:
Customer Uploads: Data submitted via the Emvide portal by the client.
Practitioner Input: Data provided by a qualified LCA practitioner.
Direct Integration: Automated connections to external systems (e.g., ERP platforms).
Advantages:
Provides the most accurate and specific insights into operational impacts.
Captures the nuances of unique processes and materials.
2. Secondary Data
Secondary data is used to fill gaps where primary data is unavailable or incomplete. These datasets are sourced from:
Customer-Provided Verified Datasets: Including prior LCAs or Environmental Product Declarations (EPDs).
Key Features:
Emissions factors include Global Warming Potential (GWP) values for materials and processes.
Data Validation
Emvide incorporates a multi-layered validation process to ensure data integrity:
1. Automated Validation
Emvide's AI performs the following checks:
Consistency: Verifies logical coherence across datasets, ensuring that material and energy flows align with lifecycle stages.
Representativeness: Assesses data relevance based on geographic, temporal, and technological contexts.
Completeness: Flags missing data and identifies gaps in material or energy flows.
2. Practitioner Oversight
Experienced LCA practitioners review flagged anomalies and refine the dataset as needed. Adjustments include:
Adding supplementary data.
Revising assumptions for specific lifecycle stages.
Justifying exclusions or deviations from the standard workflow.
Quality Assurance Framework
Emvide employs a five-element data quality framework to assess input reliability:
Technology: Ensures data aligns with the processes and technologies being assessed.
Time: Verifies that data is relevant to the reporting year.
Geography: Confirms that data reflects the locations where lifecycle activities occur.
Completeness: Evaluates whether all significant inputs and outputs are captured.
Reliability: Assesses confidence in the data source, favouring primary over secondary data.
Transparency and Documentation
Traceability: All assumptions, adjustments, and data sources are documented in the appendix of each report.
Feedback Loops: Emvide provides automated feedback to customers or practitioners, highlighting areas requiring additional clarity or refinement.
Knowledge Base: Access more details about Emvide’s validation framework at Data Validation.
How Data Quality Impacts Results
High-quality data ensures that:
Results are robust and reliable.
Emissions hotspots are accurately identified.
Recommendations are actionable and specific.
Reports meet regulatory and stakeholder requirements.
Additional Resources
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