When reviewing your data quality, keep in mind that there are 3 aspects to “good” utility data: Completeness, Accuracy, & Quality. This article will review these three keys to quality data, and how you can go about assessing and mending your data.
Completeness is the measure of how much data you have compared to how much you should expect to have.
- Time (Meter completeness)
- Space (Floor area completeness)
This means that you want to look at how many months of data you have, and how much of a property’s square footage is covered by the data you’re tracking. Depending on your portfolio type, 100% completeness might not be attainable, but you should certainly strive to maximize wherever you can.
You can refer to your meter completeness metrics under Portfolio Trends, and the floor area completeness metrics under Data Completeness. You can also get a clear picture of what months don’t have data by downloading the Data Quality Report, and checking the Monthly tab on the export.
Accuracy can be measured based on how well the data in Measurabl matches up with the source of data, which most of the time is a utility bill or a utility provider website.
If you’re using Utility Sync, Measurabl's Data Ops team is consistently assessing meter reading accuracy, but you can also review Utility Sync data yourself.
If you’re manually entering data into Measurabl or another system that pushes into Measurabl like Energy Star Portfolio Manager, it’s essential that you know the source of truth behind the data and make comparisons to it regularly.
NOTE: Another important facet to accuracy is the lifecycle stages of assets. This can tell us when consumption patterns are expected to change or when data might not be available. Make sure you are keeping these updated in Measurabl.
After completeness and accuracy are addressed, you’ll have a clear view into your outliers, which can be evaluated based on period-over-period trends and intensity rates.
Both of these metrics can be found on the Portfolio Trends page by selecting the metric and period for evaluation and scrolling to the Site Breakout table. Note that you may need to use the ‘Columns’ button to activate the Normalization (intensity) column.
Our Data Quality Alerts are also a useful tool for identifying issues. If you prefer digging into the monthly details, you can download the Data Quality Report, and check the Monthly tab.
Questions to ask as you’re reviewing outliers:
- Did this property see any changes to occupancy or tenants?
- Did we implement any efficiency measures that would reduce our usage?
- Did we have any issues with leaks or running toilets?
- Are we capturing data for all of the right meters?
- Are meters accurately assigned to spaces?