We want the data in the Secaro platform to be useful and accurate. This article explains the automated guardrails in place and common sources of anomaly.
Anomalous data may be unexpectedly high, unexpectedly low, or significantly different compared to a trend. Anomalous data does not mean erroneous data. It means data that requires further investigation to determine whether it is erroneous or not.
Secaro’s role is to facilitate the flow and exchange of data. We support data quality with automated guardrails (see below), which are subject to ongoing evaluation and refinement.
Automated guardrails
Secaro provides suppliers with automated guardrails to help maintain the quality of data entered in the platform relating to energy and GHG emissions includes:
Soft guards (Warnings)
These are designed to flag potentially unusual entries while still allowing suppliers to proceed.
- For energy and GHG emissions data, a warning is triggered if a value entered for a specific energy type is more than ±20% compared to the previous year OR if it exceeds a warning threshold.
- These checks are automated at the individual data-point level, and the warning is visible to the supplier at the point of entry.
- An example is shown in the screenshot below, where the system highlights the value and prompts the supplier to review or verify the entry.
Hard guards (No-go thresholds)
These prevent submission of values that exceed predefined acceptable limits.
- If a value exceeds the no-go threshold, the entry cannot be saved or submitted.
- In such cases, supplier input requires review and approval by Secaro, including a plausibility check by our sustainability team. If there is evidence to support the plausibility of the data, Secaro generates a validation exception, allowing the data to be saved and shared.
- As shown in the screenshot below, the system blocks the entry and displays a message indicating that the value exceeds expected limits and requires further action.
Limitation to automated guardrails
Automated guardrails do not capture all potential data anomalies. If, based on your knowledge of a supplier, you identify unexpected results, we encourage you to query these with suppliers directly, using the comment functionality on your Data Requests management page. Common sources of anomalies are:
a) Allocation ratios
All shared numeric environmental data, including energy, emissions, water and waste, is allocated to customers based on the reported ratio of a supplier’s sales that can be attributed to each buyer. This ratio (%) is commercially sensitive and therefore isn't visible to buyers, who only see the resultant allocated amounts. All suppliers are asked what method was used to determine the allocation share, and this can be viewed in your Analyze>Reported Data page.
b) Low energy inputs
Anomalously low energy consumption inputs can be as material as anomalously high inputs, but can be more difficult to identify with certainty.
c) Scope 3 estimates
To estimate supplier upstream Scope 3 emissions, Secaro uses a default spend-based database, based on Exiobase, which provides indicative estimates for upstream Scope 3 emissions. The results reflect an amount of product type sold to you as a customer, and are reliant on your supplier entering accurate sales data. As a global analysis, the method makes many assumptions about the representative technologies, economic activities, and emissions intensities of industries in different countries and regions that may not be representative of individual supplier circumstances.
If alternative, more reliable data is available, Secaro encourages suppliers to use their own estimates of their upstream emissions (e.g. from their own Scope 3 inventory, life cycle assessment, or product carbon footprint). Secaro undertakes basic checks on this information to ensure completeness or prevent double-counting of emissions before integrating into the platform.