When reviewing outliers in Data Manager, follow these steps to ensure data accuracy and integrity. See here for an overview of Data Manager and the different issues you may encounter.
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Prioritize Buildings or Meters with High Impacted Usage
- Identify High-Impact Buildings: Start by focusing on buildings with the greatest impacted usage associated with outliers through the Buildings view under Manage. This helps in addressing the most significant data quality issues first.
- Identify High-Impact Meters: Users can also utilize the same approach but at the meter level through the Meters view under Manage.
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Examine Individual Meter Reading Issues
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Investigate Outliers: Review individual meter reading outliers by navigating to the Outliers table under Check. The following fields will be useful in validating the issue before resolving:
- Issue: the exact issue we’ve detected for this reading, such as high usage or cost. Hovering over the tooltip will provide more information as well.
- Usage: the current, recorded usage for this meter reading.
- Expected Usage: AI-powered estimate of expected usage based on historical trends and weather patterns.
- Cost: the current, recorded cost value for this meter reading.
- Reading Last Updated By: who last updated this meter reading. This could be a specific user or a system.
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Reading Source: where the meter reading is sourced from, like Connect or ENERGY STAR®.
- Note: outliers for Connect and Bill Upload meter readings are reviewed and resolved by the Measurabl team.
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Investigate Gaps: Review meter reading gaps by navigating to the Gaps table under Check. The following fields will be useful in validating the issue before resolving:
- Issue: the exact issue we’ve detected for this reading.
- Period Start & End Dates: the range of missing data we’ve detected a gap for. The lack of an end date indicates this is an End Gap.
- Expected Usage: AI-powered estimate of expected usage based on historical trends and weather patterns. Helpful when prioritizing based on the impact this missing data has on your portfolio data completeness.
- Action Needed: based on the meter reading source and other attributes, this is the recommended action to take.
- Latest Reading Created By: who added the most recent meter reading, which may help determine who needs to add this missing meter reading.
- Latest Reading Source: where the most recent meter reading was sourced from, like Connect or ENERGY STAR®.
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Investigate Outliers: Review individual meter reading outliers by navigating to the Outliers table under Check. The following fields will be useful in validating the issue before resolving:
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Review and Correct Data in Core
- Assess Data in Core: After identifying the gaps and outliers in Data Manager, review the corresponding data in the Core application by navigating to the appropriate building & meter, and subsequently reviewing the individual meter readings that have been flagged as outliers. This step is crucial for further assessing the data and making any necessary corrections.
- Tip: You can have both the Core and Quality Check applications open simultaneously in separate tabs to be able to review the data easily.
- Make Corrections: If a transcription error or any other data issue is found, make the required corrections in the Core application. You can also add missing meter readings as needed. See here on how to add/edit manual data readings. This will help resolve the gap or outlier and improve overall data quality.
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Outlier Detection will Refresh Daily
- Changes made to data in the Core application that remove or correct meter reading outliers will be reflected in the Quality Check application on the next daily refresh of the meter reading outliers. Outliers that are no longer detected are automatically considered resolved. Once resolved, the outlier will no longer be visible in the Quality Check application. Outliers are refreshed every 24 hours at 10AM UTC (6AM ET, 3AM PT).