Understanding data completion and alerts

This article explains how Scaler's data quality system works — what Scaler Data Completion measures, how the three alert types interact with it, and what each alert type means for your data.

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Scaler Data Completion reflects whether all required and conditionally required fields for an asset are correctly populated and valid.

When an asset reaches 100% Scaler Data Completion:

Data completion is evaluated structurally — not based on whether consumption values have been entered.

Three things to understand before you start

1. You can reach 100% without any meters or consumption

You do not need to create meters or add consumption data to reach 100%. As long as all required asset-level fields are complete and valid, data completion can reach 100%.

2. Meters only affect completion if they exist and are incorrect

Once meters are created, Scaler evaluates them. If a meter triggers an Error or Missing data alert, that alert reduces completion. If no meters exist, meter rules do not apply.

3. Incorrect consumption can reduce completion — but consumption is never required

If consumption is added and it contains errors (overlapping dates, missing units, invalid ranges), those errors will reduce completion. You are never required to add consumption to reach 100%.

Scaler checks data completion at three structural levels.

Asset level

Checks whether all required and conditionally required asset fields are valid, including Asset name, Client ID, Asset unit system, Main property type, Main property subtype, Country, Owned since, Active in analytics, Active in reporting outputs, and 3 Gross floor area fields.

Meter level (only applies once meters exist)

Checks each meter for correct configuration: Subcategory, Source, Covered area, Monitoring method, valid meter version dates, and no overlapping consumption periods.

Portfolio level

Portfolio completion is the average completion score across all active assets. A single incomplete asset reduces the overall percentage.

Scaler generates three types of alerts when data doesn't meet required standards.

Errors

Errors occur when entered data violates validation rules, input requirements, or reporting logic.

Common general errors:

Common report-specific errors (e.g. GRESB):

Missing data

Missing data alerts occur when a required or conditionally required field is empty.

For Scaler analytics, this typically includes missing Gross floor area, Common area, or Tenant area — these prevent calculation of coverage and intensities.

For reporting outputs, it includes fields required by specific frameworks (e.g. Percentage green missing under Meter Details for GRESB).

Warnings

Warnings flag situations that may need attention but do not block calculations and do not reduce Scaler Data Completion. A warning does not mean data is wrong — it means Scaler has detected something you may not have noticed and should verify.

There are two types of warnings.

Rule-based warnings

These flag logical inconsistencies or misalignments between related fields, meters, or records. Scaler handles the situation where possible but surfaces the warning so you can review it.

Examples:

Consumption outlier warnings

These flag unusual year-over-year changes in consumption that exceed expected thresholds.

A warning is triggered if the result falls outside [-0.5, +0.5] for energy, or [-0.5, +1.0] for water and waste — aligned with GRESB methodology. If the change is expected (building closure, occupancy change, operational disruption), no action is required, but you may want to document the context.

Alerts are visible throughout the Data Collection Portal:

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