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 (or worse, after your fund manager has already reviewed your submission), you can spot and fix potential issues right as you're entering data. This saves time, reduces back-and-forth with your fund manager, and gives you confidence in your ESG data quality.
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 company has previous submissions from earlier reporting periods.
Note: If you don't see this button, it means your company 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 company'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 your 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 company previously reported quarterly but your fund manager is now requesting monthly data, 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 if your fund manager changes your 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, it's much easier to fix now than after your fund manager has flagged it or after sign-off.
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.
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 your fund manager catching them later.
Build confidence: See your data in context to feel confident about accuracy before you sign off.
Save time: Copy stable data and focus your team's attention on entries that have genuinely changed.
Learn patterns: Understand your company's own ESG trends over time.
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 certain data gatherers or metrics consistently trigger outlier alerts, it might be worth a quick training refresh or a look at your data collection process.
Document significant changes: When you have a legitimate outlier, add notes to explain the variance — this helps your fund manager understand the change without extra emails, and gives you an audit trail for the future.
FAQs
Q: Does the historical comparison feature work for custom metrics my fund manager or our company has added?
No, at the moment this only works for metrics from the KEY ESG Metric Library.
Q: Will my fund manager see that I got an outlier alert?
The alert itself is shown to you during data entry so you can review before submitting. If you'd like to flag a legitimate change proactively, add a note to the entry so your fund manager has context when they review your submission.
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.