Monitoring data quality

Assessing data quality ensures accuracy in reporting emissions standards within your supply chain operations. The Data quality dashboard shows industry-standard metrics that calculate product carbon footprint (PCF) data to improve quality, lower variance, and increase primary data ratios. The thresholds of these metrics are configurable by setting organization-specific tenant parameters. You can open, assign, and close work queues by using the information that is provided in this dashboard.

Lack of accurate, granular, and verified primary emissions data makes the reliability of supplier-provided scope 3 data difficult to manage. Envizi™ Supply Chain Intelligence allows suppliers to self-report the quality level of each PCF calculation.

You can assess the quality of PCF data on the Data quality dashboard. The following conditions indicate data quality issues in PCF data:
  • Use of low quality calculation methods.
  • High variance across suppliers of the same product. High variance indicates a lower level of confidence in the data.
  • Low ratio of primary data sources to secondary data sources.

By capturing these quality indicators from suppliers, you can assess and operationalize data quality improvements, which in turn helps your organization increase the reliability and credibility of your scope 3 calculations and reporting.

Low data calculation methods

Review the number of suppliers that are using a low quality calculation method. Calculation methods that rely on spend-based factors at the commodity or subcommodity level are low quality methods. Calculation methods that rely on lifecycle analysis are high-quality methods. Lifecycle analysis involves calculating the emissions that are produced during the entire lifecycle of a product, including the contribution of scope 1 and scope 2 emissions for the supplier of the product.
Figure 1. Low quality calculation method
Chart that shows the percentage of PCF that was collected by a low quality calculation method.
Click Improve calculation method to open the Data quality issues work queue. The Data quality issues queue is filtered to show products for suppliers that used a low quality calculation method to calculate PCF. Use the instructions on the Resolution page for the issue to learn how to work with suppliers to improve their calculation methods. Calculation methods are ranked in the following order, starting with the highest quality technique:
  1. Supplier LCA
  2. Sector LCA
  3. Sub-commodity data
  4. Commodity data
For a description of the PCF calculation methods, see Concepts.

High variance

Suppliers provide a variance value for each PCF to indicate their confidence level with the PCF value. A high level of variance indicates a lower level of confidence in the PCF data.

Figure 2. High variance
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Click Lower variance to open a work queue. The Data quality issues queue is filtered to show issues with high variances.

Low primary data ratio

The primary data ratio refers to the percentage of PCF emissions that were calculated by using primary activities and emission data.

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Click Increase primary data ratio to open a work queue. The Data quality issues queue is filtered to show suppliers with low primary data ratio.