What this feature does for you
Historical comparisons help you and your team catch data entry errors before they become problems. Instead of discovering inconsistencies later in your reports, you can spot and fix potential issues right as you're entering data. This saves time, reduces errors, and gives you confidence in your ESG data quality, all the more important when your organisation is managing its own oversight end-to-end, without a fund manager reviewing submissions on your behalf.
How to access historical comparisons
When you or one of your data gatherers is entering data for any data points in the Data Collection section, look for the "Compare historical data" button below the data entry options. This button only appears for data points where your organisation has previous submissions from earlier reporting periods.
Note: If you don't see this button, it means your organisation is reporting on this data point for the first time, so there's no historical data to compare against yet.
Understanding your historical data view
When you click "Compare historical data," you'll see your organisation's previous entries from all available years and reporting periods. This gives you context to assess whether your current data entry looks reasonable compared to past submissions, useful if a data gatherer is new to a metric or picking up where a colleague left off.
The historical data view initially defaults to the most recent data submission.
Once you change the comparison to a different time period, that selection is remembered for this particular question.
Smart period comparisons
The system automatically adjusts for different reporting frequencies. For example:
If your organisation previously reported quarterly but you've since switched to monthly reporting, you'll see the monthly equivalent of your quarterly submissions.
If you reported annually last year but are now reporting quarterly, the system shows the appropriate quarterly breakdown.
This ensures you're always comparing like-with-like, even when you decide to change your own reporting cadence between years.
Spotting and handling data outliers
As you enter numerical data, the system automatically checks if your entry falls significantly outside your historical range. Specifically, if your new entry is 25% higher or lower than comparable previous submissions, you'll see a visual alert.
Please note: this 25% comparison only works for Carbon accounting data.
Outliers aren't always errors. They might indicate:
Legitimate business changes: New facilities, changed operations, or seasonal variations.
Reporting improvements: More accurate data collection methods.
Actual errors: Typos, wrong units, or misplaced decimal points.
What to do: Review your entry and ask yourself:
Does this change make sense given what happened in your business this period?
Are you using the right units (tonnes vs. kilograms, etc.)?
Did you enter the number correctly?
If the change is legitimate, proceed with confidence. If something seems off, double-check your data before submitting — since your organisation owns sign-off end-to-end, catching this now is your best (and sometimes only) checkpoint before the data is locked and reported externally.
Copying data from previous periods
For data that hasn't changed significantly or follows predictable patterns, you can bulk-copy rows from your previous submissions and then modify only what's different, a good time-saver for facility data or recurring purchases that stay fairly stable period to period.
How to copy previous data:
In the historical comparison view, find the previous submission you want to copy.
Click the "Copy previous data" button.
The data will populate in your current period's entry fields.
Edit any values that have changed for the current period.
Submit as normal.
When copying is useful:
Facility data that remains relatively stable.
Recurring purchases or activities.
Baseline operations that don't vary significantly period-to-period.
Pairing this with your own Data Validation rules
As a standalone organisation, there's no fund manager setting Data Validation rules above you — any rules in place are the ones your own Admins have configured. It's worth setting up a few Year-on-Year Variance or Metric vs. Threshold rules for your key metrics (especially GHG Scope 1, 2 and 3) so that anomalies are flagged automatically in the background, not just at the point of data entry.
Why this helps your ESG reporting
This feature shifts quality control from reactive to proactive:
Catch errors early: Fix issues during data entry instead of discovering them after the year is locked.
Build confidence: See your data in context to feel confident about accuracy before your own sign-off.
Save time: Copy stable data and focus your team's attention on entries that have genuinely changed.
Learn patterns: Understand your organisation's ESG trends over time, since you're the only ones tracking them.
Getting the most from historical comparisons
Use it as a sense-check: Historical data should inform your current entries, not constrain them. Legitimate business changes should be reflected in your data.
Pay attention to patterns: If you consistently see outliers in certain areas, it might indicate a need for process improvements or additional training for data collectors.
Document significant changes: When you have a legitimate outlier, add notes to explain the variance. Since you don't have a fund manager to loop in, this note is often your only record of why a number changed — valuable for your own audit trail and for anyone in your organisation reviewing the data later.
FAQs
Q: Does the historical comparison feature work for custom metrics?
No, at the moment this only works for metrics from the KEY ESG Metric Library.
Q: Who reviews outlier alerts if we don't have a fund manager?
It's entirely up to your organisation's internal process. We'd recommend an Admin or whoever owns sign-off periodically checks the Data Validation tab (not just relying on the in-the-moment alert data gatherers see), so nothing slips through if a data gatherer proceeds without flagging a genuine issue.
Need help?
If you have questions about interpreting your historical data or would like guidance on handling outliers, our support team is here to help at support@keyesg.com. We're excited about this improvement and believe it will give your team even more confidence in the accuracy and consistency of your ESG data.