Purpose
This article explains how Scaler evaluates data reliability: how closely reported consumption represents actual energy, water, or waste use over time.
Scaler assesses data reliability based on the Monitoring method used to obtain consumption values, in line with a PCAF-aligned methodology and market practices used by frameworks such as GRESB and INREV.
Scaler calculates a Data reliability Score (0–100) at the asset level for:
- Energy
- Water
- Waste
This can be found at Data Collection Portal → Portfolio → Reports.
The score helps users understand the confidence level of reported KPIs, particularly for benchmarking, trend analysis, and ESG disclosures.
Understanding data quality vs data reliability
Before diving into reliability scoring, it's important to understand how it differs from data quality:
- Data quality refers to whether input data is correct, complete, and valid at the point of entry
- Managed through Scaler's alert system (errors, missing data, warnings)
- Ensures data meets basic standards before calculations run
- See Alerts in Scaler: errors, missing data, and warnings for details
- Data reliability refers to how representative and trustworthy consumption data is over time
- Evaluated through monitoring method scoring and coverage analysis
- Helps users understand confidence in reported KPIs
- This article focuses on reliability methodology
Both work together: quality checks ensure valid inputs, while reliability scores indicate how well those inputs represent actual consumption.
What is data quality?
Data quality describes whether your data is:
- Complete – all required fields are filled in
- Accurate – values are correct and properly formatted
- Consistent – logic and values align across the portfolio
- Valid – data passes required validation and reporting rules
High data quality ensures that analytics and reports are built on clean, usable inputs.
How Scaler supports data quality
Scaler supports data quality through a combination of structural controls and automated checks:
- Required fields and controlled dropdowns for standardization
- Built-in validation rules to flag invalid or illogical entries
- Automated alerts for:
- Errors (invalid values)
- Missing data
- Warnings (outliers or inconsistencies)
- Outlier detection to highlight unusual changes in resource use
Together, these mechanisms help ensure data is reliable enough for analytics and reporting from the start.
What is data reliability?
Data reliability reflects how closely reported consumption represents actual energy, water, or waste use over time. Scaler assesses data reliability based on the Monitoring method used to obtain consumption values, in line with a PCAF-aligned methodology and market practices used by frameworks such as GRESB and INREV.
Scaler calculates a Data reliability Score (0–100) at the asset level for:
- Energy
- Water
- Waste
This can be found at Data Collection Portal → Portfolio → Reports.
The score helps users understand the confidence level of reported KPIs, particularly for benchmarking, trend analysis, and ESG disclosures.
Next to the asset level bar graphs reflecting data reliability, Scaler also displays an annual trend graph of progress at portfolio level to give insight where improvements in data coverage or monitoring could be made.
How data reliability is determined
Data reliability is based on three primary factors:
Monitoring method
Area coverage– how much of the asset’s floor area is covered
Time coverage– how many days in the reporting period have valid data
The overall reliability score is a weighted average based on Monitoring method and how much area and time they cover.
Monitoring method scoring
Different monitoring methods contribute differently to the data reliability score and are related to whether consumption data is categorized as actual or estimated.
Monitoring method | Score | Actual vs estimated consumption |
Smart meters | 1.0 | Actual |
Invoices / Conventional meters | 0.8 | Actual |
estimation_(sjv_cluster) | 0.6 | Actual |
estimation_(sjv_postal_code) | 0.4 | Estimated |
estimation_(calculation) | 0.2 | Estimated |
Note: The final asset score reflects both the meter type and the proportion of area and time covered by each method.
Actual vs estimated consumption data
Actual data is refers to monitoring methods that reflect data which is directly measured at the asset level or derived from aggregated actual consumption provided by grid operators.
Estimated data is refers to monitoring methods that reflect data which is modelled, averaged, or calculated without direct measurement of the asset.
Country-specific standardized consumption systems
In some countries, standardised consumption systems are used that aggregate actual meter data to provide benchmarks.
Netherlands: Standaard Jaarverbruik (SJV)
The Netherlands uses SJV data provided by grid operators, which comes in two forms:
- SJV Cluster data (
estimation_(sjv_cluster)): Aggregated actual consumption from similar asset types (e.g., office buildings of similar size). This is derived from real meter readings of comparable assets and is treated as actual consumption with a score of 0.6.
- SJV Postal Code data (
estimation_(sjv_postal_code)): Averaged consumption across all building types within a postal code area. This broader averaging means it is treated as estimated consumption with a score of 0.4.
Although both methods are labeled with "estimation" in their monitoring method names, SJV Cluster data is considered actual because it reflects aggregated real measurements from comparable assets, making it more reliable for PCAF-aligned scoring.
Other countries
Comparable systems may exist in other countries where grid operators provide aggregated or standardized consumption data at asset-type or connection level. Where such systems are methodologically similar to SJV, clients may use the corresponding monitoring method options in Scaler, provided this approach accurately represents how the consumption data was obtained.
Explaining anomalies
Scaler flags unusual consumption trends and allows users to add contextual comments directly in the platform.
This supports transparency around:
- Building closures
- Occupancy changes
- Operational disruptions
- Other one-off events
Why data quality and reliability matter
Together, data quality validation and data reliability scoring help ensure that:
- Inputs are correct from the start (via quality checks)
- KPIs can be trusted over time (via reliability scoring)
- ESG reporting is defensible and auditable
- Decision-making for decarbonisation pathways is better informed
Related: To understand how Scaler validates data at the point of input through automated alerts, see Alerts in Scaler: errors, missing data, and warnings.