Data flags are provided to help you identify potential data quality issues.
Data flags highlight potential anomalies with the data you have reported. They are generated automatically by analysing trends in your reported data, e.g. a data flag could be triggered by a significant increase or decrease in energy consumption reported for consecutive years.
It is possible for a data flag to be triggered by valid and robust data, e.g. your organisation may have dramatically reduced its consumption because it implemented energy-saving measures or because of operational or service level changes.
It is also possible for an organisation to have serious data errors that do not trigger data flags.
SEAI recommends that you review all active data flags, investigate the underlying data and take action, if appropriate.
However, there is no obligation to take action on foot of a data flag.
Having an active data flag does not, in itself, affect your organisation's reporting status.
Data flags are triggered by calculation rules that are based on the data you report and on the size of your organisation (in terms of its overall energy consumption).
A yellow flag indicates that a certain threshold has been exceeded, whereas an orange flag indicates that a higher threshold has been exceeded.
The rules are summarised in the table below.
Remember that a data flag does not necessarily mean that your data is incorrect, i.e. a flag can be triggered by a data trend that is fundamentally correct.
Data flag rule
Organisations <500,000 kWh
Organisations 500,000-5,000,000 kWh
Organisations >5,000,000 kWh
Yellow
Orange
Yellow
Orange
Yellow
Orange
Change total energy consumption since previous year
15-30%
>30%
10-20%
>20%
5-10%
>10%
Change thermal consumption since previous year
15-30%
>30%
10-20%
>20%
5-10%
>10%
Change transport consumption since previous year
15-30%
>30%
10-20%
>20%
5-10%
>10%
Change electricity consumption since previous year
15-30%
>30%
10-20%
>20%
5-10%
>10%
Ratio of thermal to electricity consumption in year
Data flag rules evaluate data reported over three years:
Before the meter consumption data becomes available to you via the M&R system (aka the provisional scorecard date), the data flag rules focus on the data reported via the previous reporting cycle for the three years up to that reporting cycle.
From the provisional scorecard date onward, the data flag rules focus on the data reported via the current reporting cycle for the three years up to the current reporting year.
For the 2025 reporting cycle, the meter consumption data for 2025 will be available via the M&R system by 10 April 2026 (the provisional scorecard date). Prior to this, the data flag rules focus on data reported for the years 2022-2024, with relevant rules being triggered by changes in consumption between 2022-2023 and 2023-2024. After 10 April 2026, when the 2025 meter data is live in the system, the data flag rules focus on data reported for the years 2023-2025, with relevant rules being triggered by changes in consumption between 2023-2024 and 2024-2025.
If the source of an anomaly is immediately obvious to you from the data flag, the easiest way to confirm this may be to go directly to the relevant data input screen and check a value that you reported. For example, if a data flag highlights a 150% increase in transport consumption since the previous year and this strikes you as being obviously incorrect, you could go to the energy use screen and check the value reported for road diesel.
Very often, the underlying reason for a data flag may be less apparent. In these circumstances, the best way to investigate is to review your data reports, by clicking on 'review performance':
The energy & GHG breakdown report is often the best place to start because it presents a breakdown of your reported consumption in graphical and tabular formats.
You should focus your investigation on the data presented in the final energy consumption tab in this report. (This is because the data flag rules are triggered by values reported for final energy consumption. The values shown in the other tabs in this report (weather-adjusted consumption, primary energy and CO2 emissions) are all calculated values that can be affected by changes in various conversion and emission factors over time.)
Remember that you can download all of the above reports in spreadsheet format for more detailed analysis.
Note that the key indicators and other scorecard-type reports are often less useful for diagnosing data anomalies because these reports show calculated results that can be affected by significant changes in conversion and emission factors over time. However, the energy & GHG targets - key indicators is useful for seeing the impact of the data you have reported (whether correct or anomalous) on the overall performance indicators calculated for your organisation .
This data flag can only be triggered by significant changes reported for annual activity metric value that is used to calculate your organisation's energy performance indicator.
If your organisation uses a simple activity metric, then it is a change in this value that triggers the rule.
If your organisation uses a composite activity metric, then it is a change in the overall composite value that triggers the rule.
If the source of an activity metric data anomaly is immediately obvious to you from the data flag, the easiest way to confirm this may be to go directly to the activity metric values data input screen and check a value that you reported.
If you use a composite activity metric:
You can review the calculated composite activity metric value in the energy targets - detailed data data report by clicking on the ‘activity’ tab and the ‘activity metrics’ sub-tab.
These flags are triggered if you report buildings with small floor areas. As with all data flags, the underlying data may be robust, i.e. if you have very small buildings in your portfolio.
If a data flag highlights an anomaly that is immediately obvious to you, the easiest way to confirm this may be to go directly to the building register data input screen and check a value that you reported.
If you have a large portfolio of buildings or the source of a potential floor area anomaly is less clear, you can:
Go to the buildings data report and use the floor area filter to show the buildings that have floor areas below the relevant threshold highlighted in the data flag.
Or download your building register via the building register data input screen and review the floor areas reported.
You have three options with respect to an active data flag.
¶ Correct any anomalies by editing the underling data
If you have investigated the data flag and believe that there is an error in the reported data, you should correct the data at the next available opportunity, via the relevant data input screen.
Depending on the scale of edits made, this may or may not result in the system removing the active data flag.
Note that the calculation of data flags is updated every 10 minutes, so any data edits you make will not be carried forward to the data flags screen immediately. The time of the last calculation update is shown toward the bottom of the data flags screen.
If you have investigated the data flag and believe that the underlying data is correct, you can manually clear the flag by clicking the ‘clear flag’ button in the table.
This will change the status of the flag to cleared and it will be shown in a separate table.
Note that you also have the option to restore a cleared flag if you believe that a flag has been cleared in error.
This is a valid option, but SEAI recommends undertaking one or other of the other options!
Having an active data flag does not, in itself, affect your organisation's reporting status.
¶ Data flags and data verification assessment (DVA)
Data verification assessment (DVA) is SEAI’s formal mechanism for maintaining data quality. A DVA is an assessment of specific aspect(s) of an organisation’s submission via M&R. Your organisation’s data is evaluated against data acceptability criteria. The outcome from a DVA is a formal classification of the organisation’s data submission, which has implications for how your annual performance results are published by SEAI.
While having an active data flag does not automatically result in the selection of an organisation for a DVA, the DVA selection process includes elements that are very similar to those used for data flags, e.g. identifying significant changes in reported consumption. Therefore, SEAI strongly recommends that you review all active data flags and take appropriate action to resolve any underlying data anomalies before the final reporting deadline as this helps maintain data quality, improves the robustness of performance results published for your organisation and reduces the likelihood of your organisation being selected for a DVA.
SEAI's general approach for classifying the quality of data submissions and the findings of DVAs, and for publishing savings results, is explained here.