Facility Data Manager: Outlier Detection

Table of Contents

• Accessing Outlier Detection
• Displaying FDM Dismissed Data Checks
• Month-Over-Month Outlier Detection
• FDM to FEM Outlier Detection

Accessing Outlier Detection

High-quality data is the most valuable component of the Higg Index and the Worldly platform. In order to maintain high-quality data, it is important to identify and correct any outliers. 

An Outlier is a data point or calculation that differs significantly from our historical observations and expectations.

Before posting your Assessment, you may see a message that prompts you to review outliers. Click Review Outliers to see which items you need to check.

Accessing Outlier Detection

The Outlier Detection panel will come from the right-side of the screen, which will guide you through reviewing any outliers detected in your assessment.

Under the text, you will see hyperlink(s) prompting you to review your inputs. For example, in the screenshot below you are prompted to Review Finished Product Assembler and Review Production Volume. Clicking on the link will bring you to the part of the assessment where the suspected outlier is.

Outlier detection identifies any outlier, or data point/calculation that is significantly different, that you can dismiss. These are highlighted in yellow.

For example, in this screenshot below, the normalized energy usage appears high for this facility type. Before proceeding, you must review all energy sources for errors including the reported production volume. If you’ve reviewed the data and can confirm it is accurate, check the box next to “I reviewed and it looks correct”. You can also select Review Later to dismiss the outlier.

1. Selecting I reviewed and it looks correct will open a text box where you can add a comment about dismissing the flag.
   
   Clicking Save will move the outlier to the Reviewed list. It will display who marked the outlier correct, as well as the date it was marked correct. Select the arrows next to the outlier to jump to the specific question within the assessment.
   
2. Selecting Review Later will keep the flag on the To Review list. Click the yellow tab to access the list of outliers. 
   

While you’re finishing your assessment, you can click the yellow tab to check anything that still needs to be reviewed. 

The yellow tab turns green once there are no more outliers to be reviewed.

Validations, highlighted in red, identify any errors that you must address and cannot dismiss.

For example, in this screenshot below, the energy mix of the purchased steam for the reporting year needs to add up to 100%. You will not be able to post the assessment until the value is reviewed and changed.

Once you see this message, you can post your assessment. 
NOTE: FDM and FEM require outlier checks before posting.

• Erroneous outliers are data points that arise from errors during data collection, recording, calculation, or entry. They represent inaccuracies and do not reflect true underlying patterns in the data. For example, a typographical error that records a person’s age as 250 years instead of 25 would be an erroneous outlier, as it does not correspond to a realistic value. Similarly, recording an energy value in the wrong units without conversion (e.g., a value in megajoules, MJ, assigned units of kilowatt-hours, kWh) also leads to erroneous outliers. Such “bad data” must be identified and addressed – typically by removal or correction (e.g., via verification or replacement with imputed values) – to maintain the integrity of the analysis.

• True outliers are legitimate data points that are statistically rare but not erroneous. They occur naturally in the data and reflect real phenomena or occurrences. True outliers often provide valuable information about unusual but valid behaviors or events. For example, an anomalous increase in facility energy use might correspond to increased cooling implemented during a heat wave. True outliers can reveal critical trends, risks, or opportunities and should not be dismissed without analysis.

Displaying FDM Dismissed Data Checks

Displaying dismissed data checks allows the monitoring of data quality issues that might have been overlooked by the facility.

Verifying bodies validating an FDM submission can review dismissed data checks using the following instructions:

1. Click on the blue hyperlinked Account Name of the submission.
   
    
2. Review who has been authorized as a verifier from the contact list. These individuals also have access to view dismissed outlier checks.
   
    
3. Click View.
   
    
4. Verify responses as usual.
   
    
5. Click on the green check mark box, this displays all outliers the facility has already reviewed as accurate examples.
   
    
6. Click in to a dismissed outlier to see the outlier and the explanation, highlighted red in the screenshot, from the facility on why they dismissed the outlier.
   
    
7. This view is also available to brands after it is in the ASC status, where the self assessment has been completed.

Month-over-Month Outlier Detection

Month-over-Month outlier detection empowers facilities to proactively manage and correct anomalies, which enhances the overall accuracy of reported FDM data.

This feature adds a set of checks to existing outlier detection by focusing on significant month-over-month increases or decreases in total energy and water usage.

Facilities, auditors, and brands can use this feature to analyze monthly usage trends.

1. You must have at least one FDM submission with energy and/or water usage data for this feature to work. This example shows 300,000 MJ of purchased electricity was reported for this FDM submission.
   
    
2. Click Submit Data.
   
    
3. Make sure the check mark next to “Populate with previous posted assessment data” has been checked and click Continue Submission.
   
    
4. Fill out your FDM submission with the required data.
   
    
5. If the reported data for energy and/or water is 167% over the previous submission, the month-over-month outlier detection check will be triggered.
   
    
6. From here you can either enter in the correct value or check the box labeled “I reviewed and it looks correct”
   
7. Once the outlier has been reviewed and saved, click the green check box to review your outlier history in the Outlier Detection window.
   
8. Once the submission is 100% completed, click Submit Facility Data and type POSTMODULE.
   
    

FDM to FEM Outlier Detection

Both FEM and FEM require outlier detection before posting.

Please note that although FDM data imported to FEM is not checked during the transfer, FEM runs outlier detection on the imported data before the assessment is posted.